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apache / solr / faa1001ae68b5ffd0bc32c77162a7ace39f66511 / . / solr / core / src / test / org / apache / solr / search / facet / TestJsonFacets.java
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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.solr.search.facet;
import com.carrotsearch.randomizedtesting.annotations.ParametersFactory;
import com.tdunning.math.stats.AVLTreeDigest;
import java.nio.ByteBuffer;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Locale;
import java.util.Map;
import java.util.Random;
import java.util.concurrent.atomic.AtomicLong;
import org.apache.lucene.tests.util.LuceneTestCase;
import org.apache.solr.JSONTestUtil;
import org.apache.solr.SolrTestCaseHS;
import org.apache.solr.client.solrj.SolrClient;
import org.apache.solr.common.SolrException;
import org.apache.solr.common.SolrInputDocument;
import org.apache.solr.common.params.ModifiableSolrParams;
import org.apache.solr.common.params.SolrParams;
import org.apache.solr.request.SolrQueryRequest;
import org.apache.solr.request.macro.MacroExpander;
import org.apache.solr.util.hll.HLL;
import org.junit.AfterClass;
import org.junit.BeforeClass;
import org.junit.Test;
// Related tests:
// TestCloudJSONFacetJoinDomain for random field faceting tests with domain modifications
// TestJsonFacetRefinement for refinement tests
// TestJsonFacetErrors for error case tests
// TestJsonRangeFacets for range facet tests
@LuceneTestCase.SuppressCodecs({
"Lucene3x",
"Lucene40",
"Lucene41",
"Lucene42",
"Lucene45",
"Appending"
})
public class TestJsonFacets extends SolrTestCaseHS {
private static SolrInstances servers; // for distributed testing
private static int origTableSize;
private static FacetField.FacetMethod origDefaultFacetMethod;
@SuppressWarnings("deprecation")
@BeforeClass
public static void beforeTests() throws Exception {
systemSetPropertySolrDisableUrlAllowList("true");
JSONTestUtil.failRepeatedKeys = true;
origTableSize = FacetFieldProcessorByHashDV.MAXIMUM_STARTING_TABLE_SIZE;
FacetFieldProcessorByHashDV.MAXIMUM_STARTING_TABLE_SIZE = 2; // stress test resizing
origDefaultFacetMethod = FacetField.FacetMethod.DEFAULT_METHOD;
// instead of the following, see the constructor
// FacetField.FacetMethod.DEFAULT_METHOD = rand(FacetField.FacetMethod.values());
// we need DVs on point fields to compute stats & facets
if (Boolean.getBoolean(NUMERIC_POINTS_SYSPROP))
System.setProperty(NUMERIC_DOCVALUES_SYSPROP, "true");
initCore("solrconfig-tlog.xml", "schema_latest.xml");
}
/** Start all servers for cluster if they don't already exist */
public static void initServers() throws Exception {
if (servers == null) {
servers = new SolrInstances(3, "solrconfig-tlog.xml", "schema_latest.xml");
}
}
@SuppressWarnings("deprecation")
@AfterClass
public static void afterTests() throws Exception {
systemClearPropertySolrDisableUrlAllowList();
JSONTestUtil.failRepeatedKeys = false;
FacetFieldProcessorByHashDV.MAXIMUM_STARTING_TABLE_SIZE = origTableSize;
FacetField.FacetMethod.DEFAULT_METHOD = origDefaultFacetMethod;
if (servers != null) {
servers.stop();
servers = null;
}
}
// tip: when debugging failures, change this variable to DEFAULT_METHOD
// (or if only one method is problematic, set to that explicitly)
private static final FacetField.FacetMethod TEST_ONLY_ONE_FACET_METHOD =
null; // FacetField.FacetMethod.DEFAULT_METHOD;
@SuppressWarnings("MathAbsoluteNegative")
@ParametersFactory
public static Iterable<Object[]> parameters() {
if (null != TEST_ONLY_ONE_FACET_METHOD) {
return Collections.singleton(new Object[] {TEST_ONLY_ONE_FACET_METHOD});
} else if (TEST_NIGHTLY) {
// wrap each enum val in an Object[] and return as Iterable
return () ->
Arrays.stream(FacetField.FacetMethod.values()).map(it -> new Object[] {it}).iterator();
} else {
// pick a single random method and test it
FacetField.FacetMethod[] methods = FacetField.FacetMethod.values();
// can't use LuceneTestCase.random() because we're not in the runner context yet
String seed = System.getProperty("tests.seed", "");
return Collections.singleton(
new Object[] {methods[Math.abs(seed.hashCode()) % methods.length]});
}
}
public TestJsonFacets(FacetField.FacetMethod defMethod) {
FacetField.FacetMethod.DEFAULT_METHOD =
defMethod; // note: the real default is restored in afterTests
}
// attempt to reproduce https://github.com/Heliosearch/heliosearch/issues/33
@Test
public void testComplex() throws Exception {
Random r = random();
Client client = Client.localClient;
double price_low = 11000;
double price_high = 100000;
ModifiableSolrParams p =
params(
"make_s",
"make_s",
"model_s",
"model_s",
"price_low",
Double.toString(price_low),
"price_high",
Double.toString(price_high));
MacroExpander m = new MacroExpander(p.getMap());
String make_s = m.expand("${make_s}");
String model_s = m.expand("${model_s}");
client.deleteByQuery("*:*", null);
int nDocs = 99;
String[] makes = {"honda", "toyota", "ford", null};
Double[] prices = {10000.0, 30000.0, 50000.0, 0.0, null};
String[] honda_models = {
"accord", "civic", "fit", "pilot", null
}; // make sure this is alphabetized to match tiebreaks in index
String[] other_models = {"z1", "z2", "z3", "z4", "z5", "z6", null};
int nHonda = 0;
final int[] honda_model_counts = new int[honda_models.length];
for (int i = 0; i < nDocs; i++) {
SolrInputDocument doc = sdoc("id", Integer.toString(i));
Double price = rand(prices);
if (price != null) {
doc.addField("cost_f", price);
}
boolean matches_price = price != null && price >= price_low && price <= price_high;
String make = rand(makes);
if (make != null) {
doc.addField(make_s, make);
}
if ("honda".equals(make)) {
int modelNum = r.nextInt(honda_models.length);
String model = honda_models[modelNum];
if (model != null) {
doc.addField(model_s, model);
}
if (matches_price) {
nHonda++;
honda_model_counts[modelNum]++;
}
} else if (make == null) {
doc.addField(model_s, rand(honda_models)); // add some docs w/ model but w/o make
} else {
// other makes
doc.addField(model_s, rand(other_models)); // add some docs w/ model but w/o make
}
client.add(doc, null);
if (r.nextInt(10) == 0) {
client.add(doc, null); // dup, causing a delete
}
if (r.nextInt(20) == 0) {
client.commit(); // force new seg
}
}
client.commit();
// now figure out top counts
List<Integer> idx = new ArrayList<>();
for (int i = 0; i < honda_model_counts.length - 1; i++) {
idx.add(i);
}
idx.sort(
(o1, o2) -> {
int cmp = honda_model_counts[o2] - honda_model_counts[o1];
return cmp == 0 ? o1 - o2 : cmp;
});
// straight query facets
client.testJQ(
params(
p,
"q",
"*:*",
"rows",
"0",
"fq",
"+${make_s}:honda +cost_f:[${price_low} TO ${price_high}]",
"json.facet",
"{makes:{terms:{field:${make_s}, facet:{models:{terms:{field:${model_s}, limit:2, mincount:0}}}}"
+ "}}",
"facet",
"true",
"facet.pivot",
"make_s,model_s",
"facet.limit",
"2"),
"facets=={count:"
+ nHonda
+ ", makes:{buckets:[{val:honda, count:"
+ nHonda
+ ", models:{buckets:["
+ "{val:"
+ honda_models[idx.get(0)]
+ ", count:"
+ honda_model_counts[idx.get(0)]
+ "},"
+ "{val:"
+ honda_models[idx.get(1)]
+ ", count:"
+ honda_model_counts[idx.get(1)]
+ "}]}"
+ "}]}}");
}
public void indexSimple(Client client) throws Exception {
client.deleteByQuery("*:*", null);
client.add(
sdoc(
"id",
"1",
"cat_s",
"A",
"where_s",
"NY",
"num_d",
"4",
"num_i",
"2",
"num_is",
"4",
"num_is",
"2",
"val_b",
"true",
"sparse_s",
"one"),
null);
client.add(
sdoc(
"id", "2", "cat_s", "B", "where_s", "NJ", "num_d", "-9", "num_i", "-5", "num_is", "-9",
"num_is", "-5", "val_b", "false"),
null);
client.add(sdoc("id", "3"), null);
client.commit();
client.add(
sdoc(
"id", "4", "cat_s", "A", "where_s", "NJ", "num_d", "2", "num_i", "3", "num_is", "2",
"num_is", "3"),
null);
client.add(
sdoc(
"id",
"5",
"cat_s",
"B",
"where_s",
"NJ",
"num_d",
"11",
"num_i",
"7",
"num_is",
"11",
"num_is",
"7",
"sparse_s",
"two"),
null);
client.commit();
client.add(
sdoc(
"id", "6", "cat_s", "B", "where_s", "NY", "num_d", "-5", "num_i", "-5", "num_is", "-5"),
null);
client.commit();
}
public void testMultiValuedBucketReHashing() throws Exception {
Client client = Client.localClient();
client.deleteByQuery("*:*", null);
// we want a domain with a small number of documents, and more facet (point) values then docs so
// that we force dvhash to increase the number of slots via resize...
// (NOTE: normal resizing won't happen w/o at least 1024 slots, but test static overrides this
// to '2')
client.add(
sdoc(
"id", "1", "f_sd", "qqq", "f_ids", "4", "f_ids", "2", "f_ids", "999", "x_ids", "3",
"x_ids", "5", "x_ids", "7", "z_ids", "42"),
null);
client.add(
sdoc(
"id", "2", "f_sd", "nnn", "f_ids", "44", "f_ids", "22", "f_ids", "999", "x_ids", "33",
"x_ids", "55", "x_ids", "77", "z_ids", "666"),
null);
client.add(
sdoc(
"id", "3", "f_sd", "ggg", "f_ids", "444", "f_ids", "222", "f_ids", "999", "x_ids",
"333", "x_ids", "555", "x_ids", "777", "z_ids", "1010101"),
null);
client.commit();
// faceting on a multivalued point field sorting on a stat...
assertJQ(
req(
"rows",
"0",
"q",
"id:[1 TO 2]",
"json.facet",
"{ f : { type: terms, field: f_ids, limit: 1, sort: 'x desc', "
+ " facet: { x : 'sum(x_ids)', z : 'min(z_ids)' } } }"),
"response/numFound==2",
"facets/count==2",
"facets/f=={buckets:[{ val:999, count:2, x:180.0, z:42 }]}");
}
public void testBehaviorEquivalenceOfUninvertibleFalse() throws Exception {
Client client = Client.localClient();
indexSimple(client);
// regardless of the facet method (parameterized via default at test class level)
// faceting on an "uninvertible=false docValues=false" field is not supported.
//
// it should behave the same as any attempt (using any method) at faceting on
// and "indexed=false docValues=false" field...
for (String f :
Arrays.asList(
"where_s_not_indexed_sS",
"where_s_multi_not_uninvert",
"where_s_single_not_uninvert")) {
SolrQueryRequest request =
req(
"rows",
"0",
"q",
"num_i:[* TO 2]",
"json.facet",
"{x: {type:terms, field:'" + f + "'}}");
if (FacetField.FacetMethod.DEFAULT_METHOD == FacetField.FacetMethod.DVHASH
&& !f.contains("multi")) {
// DVHASH is (currently) weird...
//
// it's ignored for multivalued fields -- but for single valued fields, it explicitly
// checks the *FieldInfos* on the reader to see if the DocVals type is ok.
//
// Which means that unlike most other facet method:xxx options, it fails hard if you try to
// use it on a field where no docs have been indexed (yet).
expectThrows(
SolrException.class,
() -> {
assertJQ(request);
});
} else {
// In most cases, we should just get no buckets back...
assertJQ(request, "response/numFound==3", "facets/count==3", "facets/x=={buckets:[]}");
}
}
// regardless of the facet method (parameterized via default at test class level)
// faceting on an "uninvertible=false docValues=true" field should work,
//
// it should behave equivalently to its copyField source...
for (String f :
Arrays.asList(
"where_s", "where_s_multi_not_uninvert_dv", "where_s_single_not_uninvert_dv")) {
assertJQ(
req(
"rows",
"0",
"q",
"num_i:[* TO 2]",
"json.facet",
"{x: {type:terms, field:'" + f + "'}}"),
"response/numFound==3",
"facets/count==3",
"facets/x=={buckets:[ {val:NY, count:2} , {val:NJ, count:1} ]}");
}
// faceting on an "uninvertible=false docValues=false" field should be possible
// when using method:enum w/sort:index
//
// it should behave equivalent to its copyField source...
for (String f :
Arrays.asList("where_s", "where_s_multi_not_uninvert", "where_s_single_not_uninvert")) {
assertJQ(
req(
"rows",
"0",
"q",
"num_i:[* TO 2]",
"json.facet",
"{x: {type:terms, sort:'index asc', method:enum, field:'" + f + "'}}"),
"response/numFound==3",
"facets/count==3",
"facets/x=={buckets:[ {val:NJ, count:1} , {val:NY, count:2} ]}");
}
}
@Test
public void testExplicitQueryDomain() throws Exception {
Client client = Client.localClient();
indexSimple(client);
{ // simple 'query' domain
// the facet buckets for all the requests below should be identical
// only the numFound & top level facet count should differ
final String expectedFacets =
"facets/w=={ buckets:[" + " { val:'NJ', count:2}, " + " { val:'NY', count:1} ] }";
assertJQ(
req("rows", "0", "q", "cat_s:B", "json.facet", "{w: {type:terms, field:'where_s'}}"),
"response/numFound==3",
"facets/count==3",
expectedFacets);
assertJQ(
req(
"rows",
"0",
"q",
"id:3",
"json.facet",
"{w: {type:terms, field:'where_s', domain: { query:'cat_s:B' }}}"),
"response/numFound==1",
"facets/count==1",
expectedFacets);
assertJQ(
req(
"rows",
"0",
"q",
"*:*",
"fq",
"-*:*",
"json.facet",
"{w: {type:terms, field:'where_s', domain: { query:'cat_s:B' }}}"),
"response/numFound==0",
"facets/count==0",
expectedFacets);
assertJQ(
req(
"rows",
"0",
"q",
"*:*",
"fq",
"-*:*",
"domain_q",
"cat_s:B",
"json.facet",
"{w: {type:terms, field:'where_s', domain: { query:{param:domain_q} }}}"),
"response/numFound==0",
"facets/count==0",
expectedFacets);
}
{ // a nested explicit query domain
// for all the "top" buckets, the subfacet should have identical sub-buckets
final String expectedSubBuckets = "{ buckets:[ { val:'B', count:3}, { val:'A', count:2} ] }";
assertJQ(
req(
"rows",
"0",
"q",
"num_i:[0 TO *]",
"json.facet",
"{w: {type:terms, field:'where_s', "
+ " facet: { c: { type:terms, field:'cat_s', domain: { query:'*:*' }}}}}"),
"facets/w=={ buckets:["
+ " { val:'NJ', count:2, c: "
+ expectedSubBuckets
+ "}, "
+ " { val:'NY', count:1, c: "
+ expectedSubBuckets
+ "} "
+ "] }");
}
{ // an (effectively) empty query should produce an error
ignoreException("'query' domain can not be null");
ignoreException("'query' domain must not evaluate to an empty list");
for (String raw : Arrays.asList("null", "[ ]", "{param:bogus}")) {
expectThrows(
SolrException.class,
() -> {
assertJQ(
req(
"rows",
"0",
"q",
"num_i:[0 TO *]",
"json.facet",
"{w: {type:terms, field:'where_s', "
+ " facet: { c: { type:terms, field:'cat_s', domain: { query: "
+ raw
+ " }}}}}"));
});
}
}
}
@Test
public void testSimpleSKG() throws Exception {
Client client = Client.localClient();
indexSimple(client);
// using relatedness() as a top level stat, not nested under any facet
// (not particularly useful, but shouldn't error either)
assertJQ(
req(
"q",
"cat_s:[* TO *]",
"rows",
"0",
"fore",
"where_s:NY",
"back",
"*:*",
"json.facet",
" { skg: 'relatedness($fore,$back)' }"),
"facets=={"
+ " count:5, "
+ " skg : { relatedness: 0.00699,"
+ " foreground_popularity: 0.33333,"
+ " background_popularity: 0.83333,"
+ " } }");
// simple single level facet w/skg stat & (re)sorting
for (String sort :
Arrays.asList(
"sort:'index asc'",
"sort:'y desc'",
"sort:'z desc'",
"sort:'skg desc'",
"prelim_sort:'count desc', sort:'index asc'",
"prelim_sort:'count desc', sort:'y desc'",
"prelim_sort:'count desc', sort:'z desc'",
"prelim_sort:'count desc', sort:'skg desc'")) {
// the relatedness score of each of our cat_s values is (conveniently) also alphabetical
// order, (and the same order as 'sum(num_i) desc' & 'min(num_i) desc')
//
// So all of these re/sort options should produce identical output (since the num buckets is <
// limit)
// - Testing "index" sort allows the randomized use of "stream" processor as default to be
// tested.
// - Testing (re)sorts on other stats sanity checks code paths where relatedness() is a
// "deferred" Agg
for (String limit : Arrays.asList(", ", ", limit:5, ", ", limit:-1, ")) {
// results shouldn't change regardless of our limit param
assertJQ(
req(
"q",
"cat_s:[* TO *]",
"rows",
"0",
"fore",
"where_s:NY",
"back",
"*:*",
"json.facet",
""
+ "{x: { type: terms, field: 'cat_s', "
+ sort
+ limit
+ " facet: { skg: 'relatedness($fore,$back)', y:'sum(num_i)', z:'min(num_i)' } } }"),
"facets=={count:5, x:{ buckets:["
+ " { val:'A', count:2, y:5.0, z:2, "
+ " skg : { relatedness: 0.00554, "
// + " foreground_count: 1, "
// + " foreground_size: 2, "
// + " background_count: 2, "
// + " background_size: 6,"
+ " foreground_popularity: 0.16667,"
+ " background_popularity: 0.33333, },"
+ " }, "
+ " { val:'B', count:3, y:-3.0, z:-5, "
+ " skg : { relatedness: 0.0, " // perfectly average and uncorrelated
// + " foreground_count: 1, "
// + " foreground_size: 2, "
// + " background_count: 3, "
// + " background_size: 6,"
+ " foreground_popularity: 0.16667,"
+ " background_popularity: 0.5 },"
+ " } ] } } ");
// same query with a prefix of 'B' should produce only a single bucket with exact same
// results
assertJQ(
req(
"q",
"cat_s:[* TO *]",
"rows",
"0",
"fore",
"where_s:NY",
"back",
"*:*",
"json.facet",
""
+ "{x: { type: terms, field: 'cat_s', prefix:'B', "
+ sort
+ limit
+ " facet: { skg: 'relatedness($fore,$back)', y:'sum(num_i)', z:'min(num_i)' } } }"),
"facets=={count:5, x:{ buckets:["
+ " { val:'B', count:3, y:-3.0, z:-5, "
+ " skg : { relatedness: 0.0, " // perfectly average and uncorrelated
// + " foreground_count: 1, "
// + " foreground_size: 2, "
// + " background_count: 3, "
// + " background_size: 6,"
+ " foreground_popularity: 0.16667,"
+ " background_popularity: 0.5 },"
+ " } ] } } ");
}
}
// relatedness shouldn't be computed for allBuckets, but it also shouldn't cause any problems
for (String sort :
Arrays.asList(
"sort:'y desc'",
"sort:'z desc'",
"sort:'skg desc'",
"sort:'index asc'",
"prelim_sort:'count desc', sort:'skg desc'")) {
// the relatedness score of each of our cat_s values is (conveniently) also alphabetical
// order, (and the same order as 'sum(num_i) desc' & 'min(num_i) desc')
//
// So all of these re/sort options should produce identical output (since the num buckets is <
// limit)
// - Testing "index" sort allows the randomized use of "stream" processor as default to be
// tested.
// - Testing (re)sorts on other stats sanity checks code paths where relatedness() is a
// "deferred" Agg
for (String limit : Arrays.asList(", ", ", limit:5, ", ", limit:-1, ")) {
// results shouldn't change regardless of our limit param
assertJQ(
req(
"q",
"cat_s:[* TO *]",
"rows",
"0",
"fore",
"where_s:NY",
"back",
"*:*",
"json.facet",
""
+ "{x: { type: terms, field: 'cat_s', allBuckets:true, "
+ sort
+ limit
+ " facet: { skg: 'relatedness($fore,$back)', y:'sum(num_i)', z:'min(num_i)' } } }"),
"facets=={count:5, x:{ "
// 'skg' key must not exist in th allBuckets bucket
+ " allBuckets: { count:5, y:2.0, z:-5 },"
+ "buckets:["
+ " { val:'A', count:2, y:5.0, z:2, "
+ " skg : { relatedness: 0.00554, "
// + " foreground_count: 1, "
// + " foreground_size: 2, "
// + " background_count: 2, "
// + " background_size: 6,"
+ " foreground_popularity: 0.16667,"
+ " background_popularity: 0.33333, },"
+ " }, "
+ " { val:'B', count:3, y:-3.0, z:-5, "
+ " skg : { relatedness: 0.0, " // perfectly average and uncorrelated
// + " foreground_count: 1, "
// + " foreground_size: 2, "
// + " background_count: 3, "
// + " background_size: 6,"
+ " foreground_popularity: 0.16667,"
+ " background_popularity: 0.5 },"
+ " } ] } } ");
// really special case: allBuckets when there are no regular buckets...
assertJQ(
req(
"q",
"cat_s:[* TO *]",
"rows",
"0",
"fore",
"where_s:NY",
"back",
"*:*",
"json.facet",
""
+ "{x: { type: terms, field: 'bogus_field_s', allBuckets:true, "
+ sort
+ limit
+ " facet: { skg: 'relatedness($fore,$back)', y:'sum(num_i)', z:'min(num_i)' } } }"),
"facets=={count:5, x:{ "
// 'skg' key (as well as 'z' since it's a min) must not exist in the allBuckets
// bucket
+ " allBuckets: { count:0, y:0.0 },"
+ "buckets:[ ]"
+ " } } ");
}
}
// trivial sanity check that we can (re)sort on SKG after pre-sorting on count...
// ...and it's only computed for the top N buckets (based on our pre-sort)
for (int overrequest : Arrays.asList(0, 1, 42)) {
// based on our counts & relatedness values, the blackbox output should be the same for both
// overrequest values ... only DebugAgg stats should change...
DebugAgg.Acc.collectDocs.set(0);
DebugAgg.Acc.collectDocSets.set(0);
assertJQ(
req(
"q",
"cat_s:[* TO *]",
"rows",
"0",
"fore",
"where_s:NJ",
"back",
"*:*",
"json.facet",
""
+ "{x: { type: terms, field: 'cat_s', prelim_sort: 'count desc', sort:'skg desc', "
+ " limit: 1, overrequest: "
+ overrequest
+ ", "
+ " facet: { skg: 'debug(wrap,relatedness($fore,$back))' } } }"),
"facets=={count:5, x:{ buckets:["
+ " { val:'B', count:3, "
+ " skg : { relatedness: 0.00638, "
// + " foreground_count: 2, "
// + " foreground_size: 3, "
// + " background_count: 3, "
// + " background_size: 6,"
+ " foreground_popularity: 0.33333,"
+ " background_popularity: 0.5 },"
+ " }, "
+ " ] } } ");
// at most 2 buckets, regardless of overrequest...
assertEqualsAndReset(0 < overrequest ? 2 : 1, DebugAgg.Acc.collectDocSets);
assertEqualsAndReset(0, DebugAgg.Acc.collectDocs);
}
// SKG used in multiple nested facets
//
// we'll re-use these params in 2 requests, one will simulate a shard request
final SolrParams nestedSKG =
params(
"q",
"cat_s:[* TO *]",
"rows",
"0",
"fore",
"num_i:[-1000 TO 0]",
"back",
"*:*",
"json.facet",
"{x: { type: terms, field: 'cat_s', sort: 'skg desc', "
+ " facet: { skg: 'relatedness($fore,$back)', "
+ " y: { type: terms, field: 'where_s', sort: 'skg desc', "
+ " facet: { skg: 'relatedness($fore,$back)' } } } } }");
// plain old request
assertJQ(
req(nestedSKG),
"facets=={count:5, x:{ buckets:["
+ " { val:'B', count:3, "
+ " skg : { relatedness: 0.01539, "
// + " foreground_count: 2, "
// + " foreground_size: 2, "
// + " background_count: 3, "
// + " background_size: 6, "
+ " foreground_popularity: 0.33333,"
+ " background_popularity: 0.5 },"
+ " y : { buckets:["
+ " { val:'NY', count: 1, "
+ " skg : { relatedness: 0.00554, "
// + " foreground_count: 1, "
// + " foreground_size: 2, "
// + " background_count: 2, "
// + " background_size: 6, "
+ " foreground_popularity: 0.16667, "
+ " background_popularity: 0.33333, "
+ " } }, "
+ " { val:'NJ', count: 2, "
+ " skg : { relatedness: 0.0, " // perfectly average and uncorrelated
// + " foreground_count: 1, "
// + " foreground_size: 2, "
// + " background_count: 3, "
// + " background_size: 6, "
+ " foreground_popularity: 0.16667, "
+ " background_popularity: 0.5, "
+ " } }, "
+ " ] } "
+ " }, "
+ " { val:'A', count:2, "
+ " skg : { relatedness:-0.01097, "
// + " foreground_count: 0, "
// + " foreground_size: 2, "
// + " background_count: 2, "
// + " background_size: 6,"
+ " foreground_popularity: 0.0,"
+ " background_popularity: 0.33333 },"
+ " y : { buckets:["
+ " { val:'NJ', count: 1, "
+ " skg : { relatedness: 0.0, " // perfectly average and uncorrelated
// + " foreground_count: 0, "
// + " foreground_size: 0, "
// + " background_count: 3, "
// + " background_size: 6, "
+ " foreground_popularity: 0.0, "
+ " background_popularity: 0.5, "
+ " } }, "
+ " { val:'NY', count: 1, "
+ " skg : { relatedness: 0.0, " // perfectly average and uncorrelated
// + " foreground_count: 0, "
// + " foreground_size: 0, "
// + " background_count: 2, "
// + " background_size: 6, "
+ " foreground_popularity: 0.0, "
+ " background_popularity: 0.33333, "
+ " } }, "
+ " ] } } ] } } ");
// same request, but with whitebox params testing isShard
// to verify the raw counts/sizes
assertJQ(
req(
nestedSKG,
// fake an initial shard request
"distrib",
"false",
"isShard",
"true",
"_facet_",
"{}",
"shards.purpose",
"" + FacetModule.PURPOSE_GET_JSON_FACETS),
"facets=={count:5, x:{ buckets:["
+ " { val:'B', count:3, "
+ " skg : { "
+ " foreground_count: 2, "
+ " foreground_size: 2, "
+ " background_count: 3, "
+ " background_size: 6 }, "
+ " y : { buckets:["
+ " { val:'NY', count: 1, "
+ " skg : { "
+ " foreground_count: 1, "
+ " foreground_size: 2, "
+ " background_count: 2, "
+ " background_size: 6, "
+ " } }, "
+ " { val:'NJ', count: 2, "
+ " skg : { "
+ " foreground_count: 1, "
+ " foreground_size: 2, "
+ " background_count: 3, "
+ " background_size: 6, "
+ " } }, "
+ " ] } "
+ " }, "
+ " { val:'A', count:2, "
+ " skg : { "
+ " foreground_count: 0, "
+ " foreground_size: 2, "
+ " background_count: 2, "
+ " background_size: 6 },"
+ " y : { buckets:["
+ " { val:'NJ', count: 1, "
+ " skg : { "
+ " foreground_count: 0, "
+ " foreground_size: 0, "
+ " background_count: 3, "
+ " background_size: 6, "
+ " } }, "
+ " { val:'NY', count: 1, "
+ " skg : { "
+ " foreground_count: 0, "
+ " foreground_size: 0, "
+ " background_count: 2, "
+ " background_size: 6, "
+ " } }, "
+ " ] } } ] } } ");
// SKG w/min_pop (NOTE: incredibly contrived and not-useful fore/back for testing min_pop
// w/shard sorting)
//
// we'll re-use these params in 2 requests, one will simulate a shard request
final SolrParams minPopSKG =
params(
"q",
"cat_s:[* TO *]",
"rows",
"0",
"fore",
"num_i:[0 TO 1000]",
"back",
"cat_s:B",
"json.facet",
"{x: { type: terms, field: 'cat_s', sort: 'skg desc', "
+ " facet: { skg: { type:func, func:'relatedness($fore,$back)', "
+ " min_popularity: 0.001 }"
+ " } } }");
// plain old request
assertJQ(
req(minPopSKG),
"facets=={count:5, x:{ buckets:["
+ " { val:'B', count:3, "
+ " skg : { relatedness: -1.0, "
// + " foreground_count: 1, "
// + " foreground_size: 3, "
// + " background_count: 3, "
// + " background_size: 3, "
+ " foreground_popularity: 0.33333,"
+ " background_popularity: 1.0,"
+ " } }, "
+ " { val:'A', count:2, "
+ " skg : { relatedness:'-Infinity', " // bg_pop is below min_pop (otherwise 1.0)
// + " foreground_count: 2, "
// + " foreground_size: 3, "
// + " background_count: 0, "
// + " background_size: 3, "
+ " foreground_popularity: 0.66667,"
+ " background_popularity: 0.0,"
+ " } } ] } } ");
// same request, but with whitebox params testing isShard
// to verify the raw counts/sizes and that per-shard sorting doesn't pre-emptively sort "A" to
// the bottom
assertJQ(
req(
minPopSKG,
// fake an initial shard request
"distrib",
"false",
"isShard",
"true",
"_facet_",
"{}",
"shards.purpose",
"" + FacetModule.PURPOSE_GET_JSON_FACETS),
"facets=={count:5, x:{ buckets:["
+ " { val:'A', count:2, "
+ " skg : { "
+ " foreground_count: 2, "
+ " foreground_size: 3, "
+ " background_count: 0, "
+ " background_size: 3, "
+ " } }, "
+ " { val:'B', count:3, "
+ " skg : { "
+ " foreground_count: 1, "
+ " foreground_size: 3, "
+ " background_count: 3, "
+ " background_size: 3, "
+ " } } ] } }");
}
@Test
public void testSKGSweepMultiAcc() throws Exception {
Client client = Client.localClient();
indexSimple(client);
// simple single level facet w/skg & trivial non-sweeping stat using various sorts & (re)sorting
for (String sort :
Arrays.asList(
"sort:'index asc'",
"sort:'y desc'",
"sort:'z desc'",
"sort:'skg desc'",
"prelim_sort:'count desc', sort:'index asc'",
"prelim_sort:'count desc', sort:'y desc'",
"prelim_sort:'count desc', sort:'z desc'",
"prelim_sort:'count desc', sort:'skg desc'")) {
// the relatedness score of each of our cat_s values is (conveniently) also alphabetical
// order, (and the same order as 'sum(num_i) desc' & 'min(num_i) desc')
//
// So all of these re/sort options should produce identical output
// - Testing "index" sort allows the randomized use of "stream" processor as default to be
// tested.
// - Testing (re)sorts on other stats sanity checks code paths where relatedness() is a
// "deferred" Agg
for (String sweep : Arrays.asList("true", "false")) {
// results should be the same even if we disable sweeping...
assertJQ(
req(
"q",
"cat_s:[* TO *]",
"rows",
"0",
"fore",
"where_s:NY",
"back",
"*:*",
"json.facet",
""
+ "{x: { type: terms, field: 'cat_s', "
+ sort
+ ", limit:-1, "
+ " facet: { skg: { type: 'func', func:'relatedness($fore,$back)', "
+ " "
+ RelatednessAgg.SWEEP_COLLECTION
+ ": "
+ sweep
+ " },"
+ " y:'sum(num_i)', "
+ " z:'min(num_i)' } } }"),
"facets=={count:5, x:{ buckets:["
+ " { val:'A', count:2, y:5.0, z:2, "
+ " skg : { relatedness: 0.00554, "
+ " foreground_popularity: 0.16667,"
+ " background_popularity: 0.33333, },"
+ " }, "
+ " { val:'B', count:3, y:-3.0, z:-5, "
+ " skg : { relatedness: 0.0, " // perfectly average and uncorrelated
+ " foreground_popularity: 0.16667,"
+ " background_popularity: 0.5 },"
+ " } ] } } ");
}
}
}
@Test
public void testRepeatedNumerics() throws Exception {
Client client = Client.localClient();
// docValues of multi-valued points field can contain duplicate values... make sure they don't
// mess up our counts.
String field = "num_is";
client.add(
sdoc(
"id",
"1",
"cat_s",
"A",
"where_s",
"NY",
"num_d",
"4",
"num_i",
"2",
"val_b",
"true",
"sparse_s",
"one",
field,
"0",
field,
"0"),
null);
client.commit();
client.testJQ(
params(
"q",
"id:1",
"field",
field,
"json.facet",
"{"
+ "f1:{terms:${field}}"
+ ",f2:'hll(${field})'"
+ ",f3:{type:range, field:${field}, start:0, end:1, gap:1}"
+ "}"),
"facets=={count:1, "
+ "f1:{buckets:[{val:0, count:1}]}"
+ ",f2:1"
+ ",f3:{buckets:[{val:0, count:1}]}"
+ "}");
}
public void testDomainJoinSelf() throws Exception {
Client client = Client.localClient();
indexSimple(client);
// self join domain switch at the second level of faceting
assertJQ(
req(
"q",
"*:*",
"rows",
"0",
"json.facet",
""
+ "{x: { type: terms, field: 'num_i', "
+ " facet: { y: { domain: { join: { from: 'cat_s', to: 'cat_s' } }, "
+ " type: terms, field: 'where_s' "
+ " } } } }"),
"facets=={count:6, x:{ buckets:["
+ " { val:-5, count:2, "
+ " y : { buckets:[{ val:'NJ', count:2 }, { val:'NY', count:1 } ] } }, "
+ " { val:2, count:1, "
+ " y : { buckets:[{ val:'NJ', count:1 }, { val:'NY', count:1 } ] } }, "
+ " { val:3, count:1, "
+ " y : { buckets:[{ val:'NJ', count:1 }, { val:'NY', count:1 } ] } }, "
+ " { val:7, count:1, "
+ " y : { buckets:[{ val:'NJ', count:2 }, { val:'NY', count:1 } ] } } ] } }");
}
public void testDomainGraph() throws Exception {
Client client = Client.localClient();
indexSimple(client);
// should be the same as join self
assertJQ(
req(
"q",
"*:*",
"rows",
"0",
"json.facet",
""
+ "{x: { type: terms, field: 'num_i', "
+ " facet: { y: { domain: { graph: { from: 'cat_s', to: 'cat_s' } }, "
+ " type: terms, field: 'where_s' "
+ " } } } }"),
"facets=={count:6, x:{ buckets:["
+ " { val:-5, count:2, "
+ " y : { buckets:[{ val:'NJ', count:2 }, { val:'NY', count:1 } ] } }, "
+ " { val:2, count:1, "
+ " y : { buckets:[{ val:'NJ', count:1 }, { val:'NY', count:1 } ] } }, "
+ " { val:3, count:1, "
+ " y : { buckets:[{ val:'NJ', count:1 }, { val:'NY', count:1 } ] } }, "
+ " { val:7, count:1, "
+ " y : { buckets:[{ val:'NJ', count:2 }, { val:'NY', count:1 } ] } } ] } }");
// This time, test with a traversalFilter
// should be the same as join self
assertJQ(
req(
"q",
"*:*",
"rows",
"0",
"json.facet",
""
+ "{x: { type: terms, field: 'num_i', "
+ " facet: { y: { domain: { graph: { from: 'cat_s', to: 'cat_s', traversalFilter: 'where_s:NY' } }, "
+ " type: terms, field: 'where_s' "
+ " } } } }"),
"facets=={count:6, x:{ buckets:["
+ " { val:-5, count:2, "
+ " y : { buckets:[{ val:'NJ', count:1 }, { val:'NY', count:1 } ] } }, "
+ " { val:2, count:1, "
+ " y : { buckets:[{ val:'NY', count:1 } ] } }, "
+ " { val:3, count:1, "
+ " y : { buckets:[{ val:'NJ', count:1 }, { val:'NY', count:1 } ] } }, "
+ " { val:7, count:1, "
+ " y : { buckets:[{ val:'NJ', count:1 }, { val:'NY', count:1 } ] } } ] } }");
}
public void testNestedJoinDomain() throws Exception {
Client client = Client.localClient();
client.deleteByQuery("*:*", null);
client.add(
sdoc(
"id", "1", "1_s", "A", "2_s", "A", "3_s", "C", "y_s", "B", "x_t", "x z", "z_t",
" 2 3"),
null);
client.add(
sdoc(
"id", "2", "1_s", "B", "2_s", "A", "3_s", "B", "y_s", "B", "x_t", "x y ", "z_t",
"1 3"),
null);
client.add(
sdoc(
"id", "3", "1_s", "C", "2_s", "A", "3_s", "#", "y_s", "A", "x_t", " y z", "z_t",
"1 2 "),
null);
client.add(
sdoc(
"id", "4", "1_s", "A", "2_s", "B", "3_s", "C", "y_s", "A", "x_t", " z", "z_t",
" 3"),
null);
client.add(
sdoc(
"id", "5", "1_s", "B", "2_s", "_", "3_s", "B", "y_s", "C", "x_t", "x ", "z_t",
"1 3"),
null);
client.add(
sdoc(
"id", "6", "1_s", "C", "2_s", "B", "3_s", "A", "y_s", "C", "x_t", "x y z", "z_t",
"1 "),
null);
client.commit();
assertJQ(
req(
"q",
"x_t:x",
"rows",
"0", // NOTE q - only x=x in base set (1,2,5,6)
"json.facet",
""
+ "{x: { type: terms, field: 'x_t', "
+ " domain: { join: { from:'1_s', to:'2_s' } },"
// y1 & y2 are the same facet, with *similar* child facet z1/z2 ...
+ " facet: { y1: { type: terms, field: 'y_s', "
// z1 & z2 are same field, diff join...
+ " facet: { z1: { type: terms, field: 'z_t', "
+ " domain: { join: { from:'2_s', to:'3_s' } } } } },"
+ " y2: { type: terms, field: 'y_s', "
// z1 & z2 are same field, diff join...
+ " facet: { z2: { type: terms, field: 'z_t', "
+ " domain: { join: { from:'3_s', to:'1_s' } } } } } } } }"),
"facets=={count:4, "
+ "x:{ buckets:[" // joined 1->2: doc5 drops out, counts: z=4, x=3, y=3
+ " { val:z, count:4, " // x=z (docs 1,3,4,6) y terms: A=2, B=1, C=1
+ " y1 : { buckets:[ " // z1 joins 2->3...
+ " { val:A, count:2, " // A in docs(3,4), joins (A,B) -> docs(2,5,6)
+ " z1: { buckets:[{ val:'1', count:3 }, { val:'3', count:2 }] } }, "
+ " { val:B, count:1, " // B in doc1, joins A -> doc6
+ " z1: { buckets:[{ val:'1', count:1 }] } }, "
+ " { val:C, count:1, " // C in doc6, joins B -> docs(2,5)
+ " z1: { buckets:[{ val:'1', count:2 }, { val:'3', count:2 }] } } "
+ " ] }, "
+ " y2 : { buckets:[ " // z2 joins 3->1...
+ " { val:A, count:2, " // A in docs(3,4), joins C -> docs(3,6)
+ " z2: { buckets:[{ val:'1', count:2 }, { val:'2', count:1 }] } }, "
+ " { val:B, count:1, " // B in doc1, joins C -> docs(3,6)
+ " z2: { buckets:[{ val:'1', count:2 }, { val:'2', count:1 }] } }, "
+ " { val:C, count:1, " // C in doc6, joins A -> docs(1,4)
+ " z2: { buckets:[{ val:'3', count:2 }, { val:'2', count:1 }] } } "
+ " ] } }, "
+ " { val:x, count:3, " // x=x (docs 1,2,!5,6) y terms: B=2, C=1
+ " y1 : { buckets:[ " // z1 joins 2->3...
+ " { val:B, count:2, " // B in docs(1,2), joins A -> doc6
+ " z1: { buckets:[{ val:'1', count:1 }] } }, "
+ " { val:C, count:1, " // C in doc6, joins B -> docs(2,5)
+ " z1: { buckets:[{ val:'1', count:2 }, { val:'3', count:2 }] } } "
+ " ] }, "
+ " y2 : { buckets:[ " // z2 joins 3->1...
+ " { val:B, count:2, " // B in docs(1,2), joins C,B -> docs(2,3,5,6)
+ " z2: { buckets:[{ val:'1', count:4 }, { val:'3', count:2 }, { val:'2', count:1 }] } }, "
+ " { val:C, count:1, " // C in doc6, joins A -> docs(1,4)
+ " z2: { buckets:[{ val:'3', count:2 }, { val:'2', count:1 }] } } "
+ " ] } }, "
+ " { val:y, count:3, " // x=y (docs 2,3,6) y terms: A=1, B=1, C=1
+ " y1 : { buckets:[ " // z1 joins 2->3...
+ " { val:A, count:1, " // A in doc3, joins A -> doc6
+ " z1: { buckets:[{ val:'1', count:1 }] } }, "
+ " { val:B, count:1, " // B in doc2, joins A -> doc6
+ " z1: { buckets:[{ val:'1', count:1 }] } }, "
+ " { val:C, count:1, " // C in doc6, joins B -> docs(2,5)
+ " z1: { buckets:[{ val:'1', count:2 }, { val:'3', count:2 }] } } "
+ " ] }, "
+ " y2 : { buckets:[ " // z2 joins 3->1...
+ " { val:A, count:1, " // A in doc3, joins # -> empty set
+ " z2: { buckets:[ ] } }, "
+ " { val:B, count:1, " // B in doc2, joins B -> docs(2,5)
+ " z2: { buckets:[{ val:'1', count:2 }, { val:'3', count:2 }] } }, "
+ " { val:C, count:1, " // C in doc6, joins A -> docs(1,4)
+ " z2: { buckets:[{ val:'3', count:2 }, { val:'2', count:1 }] } } "
+ " ]} }"
+ " ]}}");
}
@Test
public void testMethodStream() throws Exception {
Client client = Client.localClient();
indexSimple(client);
assertJQ(
req("q", "*:*", "rows", "0", "json.facet", "{x:'sum(num_is)'}"),
"facets=={count:6 , x:,10.0}");
assertJQ(
req("q", "*:*", "rows", "0", "json.facet", "{x:'min(num_is)'}"),
"facets=={count:6 , x:,-9}");
// test multiple json.facet commands
assertJQ(
req(
"q",
"*:*",
"rows",
"0",
"json.facet",
"{x:'sum(num_d)'}",
"json.facet",
"{y:'min(num_d)'}",
"json.facet",
"{z:'min(num_is)'}"),
"facets=={count:6 , x:3.0, y:-9.0, z:-9 }");
// test streaming
assertJQ(
req(
"q",
"*:*",
"rows",
"0",
"json.facet",
"{ cat:{terms:{field:'cat_s', method:stream }}"
+ // won't stream; need sort:index asc
", cat2:{terms:{field:'cat_s', method:stream, sort:'index asc' }}"
+ ", cat3:{terms:{field:'cat_s', method:stream, sort:'index asc', mincount:3 }}"
+ // mincount
", cat4:{terms:{field:'cat_s', method:stream, sort:'index asc', prefix:B }}"
+ // prefix
", cat5:{terms:{field:'cat_s', method:stream, sort:'index asc', offset:1 }}"
+ // offset
", cat6:{terms:{field:'cat_s', method:stream, sort:'index asc', missing:true }}"
+ // missing
", cat7:{terms:{field:'cat_s', method:stream, sort:'index asc', numBuckets:true }}"
+ // numBuckets
", cat8:{terms:{field:'cat_s', method:stream, sort:'index asc', allBuckets:true }}"
+ // allBuckets
" }"),
"facets=={count:6 "
+ ", cat :{buckets:[{val:B, count:3},{val:A, count:2}]}"
+ ", cat2:{buckets:[{val:A, count:2},{val:B, count:3}]}"
+ ", cat3:{buckets:[{val:B, count:3}]}"
+ ", cat4:{buckets:[{val:B, count:3}]}"
+ ", cat5:{buckets:[{val:B, count:3}]}"
+ ", cat6:{missing:{count:1}, buckets:[{val:A, count:2},{val:B, count:3}]}"
+ ", cat7:{numBuckets:2, buckets:[{val:A, count:2},{val:B, count:3}]}"
+ ", cat8:{allBuckets:{count:5}, buckets:[{val:A, count:2},{val:B, count:3}]}"
+ " }");
// test nested streaming under non-streaming
assertJQ(
req(
"q",
"*:*",
"rows",
"0",
"json.facet",
"{ cat:{terms:{field:'cat_s', sort:'index asc', facet:{where:{terms:{field:where_s,method:stream,sort:'index asc'}}} }}}"),
"facets=={count:6 "
+ ", cat :{buckets:[{val:A, count:2, where:{buckets:[{val:NJ,count:1},{val:NY,count:1}]} },{val:B, count:3, where:{buckets:[{val:NJ,count:2},{val:NY,count:1}]} }]}"
+ "}");
// test nested streaming under streaming
assertJQ(
req(
"q",
"*:*",
"rows",
"0",
"json.facet",
"{ cat:{terms:{field:'cat_s', method:stream,sort:'index asc', facet:{where:{terms:{field:where_s,method:stream,sort:'index asc'}}} }}}"),
"facets=={count:6 "
+ ", cat :{buckets:[{val:A, count:2, where:{buckets:[{val:NJ,count:1},{val:NY,count:1}]} },{val:B, count:3, where:{buckets:[{val:NJ,count:2},{val:NY,count:1}]} }]}"
+ "}");
// test nested streaming with stats under streaming
assertJQ(
req(
"q",
"*:*",
"rows",
"0",
"json.facet",
"{ cat:{terms:{field:'cat_s', method:stream,sort:'index asc', facet:{ where:{terms:{field:where_s,method:stream,sort:'index asc',sort:'index asc', facet:{x:'max(num_d)', y:'sum(num_is)'} }}} }}}"),
"facets=={count:6 "
+ ", cat :{buckets:[{val:A, count:2, where:{buckets:[{val:NJ,count:1,x:2.0,y:5.0},{val:NY,count:1,x:4.0,y:6.0}]} },"
+ "{val:B, count:3, where:{buckets:[{val:NJ,count:2,x:11.0,y:4.0},{val:NY,count:1,x:-5.0,y:-5.0}]} }]}"
+ "}");
// test nested streaming with stats under streaming with stats
assertJQ(
req(
"q",
"*:*",
"rows",
"0",
"facet",
"true",
"json.facet",
"{ cat:{terms:{field:'cat_s', method:stream,sort:'index asc', facet:{ y:'min(num_d)', where:{terms:{field:where_s,method:stream,sort:'index asc', facet:{x:'max(num_d)'} }}} }}}"),
"facets=={count:6 "
+ ", cat :{buckets:[{val:A, count:2, y:2.0, where:{buckets:[{val:NJ,count:1,x:2.0},{val:NY,count:1,x:4.0}]} },{val:B, count:3, y:-9.0, where:{buckets:[{val:NJ,count:2,x:11.0},{val:NY,count:1,x:-5.0}]} }]}"
+ "}");
assertJQ(req("q", "*:*", "fq", "cat_s:A"), "response/numFound==2");
}
Map<String, String[]> suffixMap = new HashMap<>();
{
suffixMap.put("_s", new String[] {"_s", "_ss", "_sd", "_sds"});
suffixMap.put("_ss", new String[] {"_ss", "_sds"});
suffixMap.put("_l", new String[] {"_l", "_ls", "_ld", "_lds"});
suffixMap.put("_ls", new String[] {"_ls", "_lds"});
suffixMap.put("_i", new String[] {"_i", "_is", "_id", "_ids", "_l", "_ls", "_ld", "_lds"});
suffixMap.put("_is", new String[] {"_is", "_ids", "_ls", "_lds"});
suffixMap.put("_d", new String[] {"_d", "_ds", "_dd", "_dds"});
suffixMap.put("_ds", new String[] {"_ds", "_dds"});
suffixMap.put("_f", new String[] {"_f", "_fs", "_fd", "_fds", "_d", "_ds", "_dd", "_dds"});
suffixMap.put("_fs", new String[] {"_fs", "_fds", "_ds", "_dds"});
suffixMap.put("_dt", new String[] {"_dt", "_dts", "_dtd", "_dtds"});
suffixMap.put("_dts", new String[] {"_dts", "_dtds"});
suffixMap.put("_b", new String[] {"_b"});
}
List<String> getAlternatives(String field) {
int idx = field.lastIndexOf('_');
if (idx <= 0 || idx >= field.length()) return Collections.singletonList(field);
String suffix = field.substring(idx);
String[] alternativeSuffixes = suffixMap.get(suffix);
if (alternativeSuffixes == null) return Collections.singletonList(field);
String base = field.substring(0, idx);
List<String> out = new ArrayList<>(alternativeSuffixes.length);
for (String altS : alternativeSuffixes) {
out.add(base + altS);
}
Collections.shuffle(out, random());
return out;
}
@Test
public void testStats() throws Exception {
doStats(Client.localClient, params("debugQuery", Boolean.toString(random().nextBoolean())));
}
@Test
public void testStatsDistrib() throws Exception {
initServers();
Client client = servers.getClient(random().nextInt());
client
.queryDefaults()
.set("shards", servers.getShards())
.set("debugQuery", Boolean.toString(random().nextBoolean()));
doStats(client, params());
}
public void doStats(Client client, ModifiableSolrParams p) throws Exception {
Map<String, List<String>> fieldLists = new HashMap<>();
fieldLists.put("noexist", getAlternatives("noexist_s"));
fieldLists.put("cat_s", getAlternatives("cat_s"));
fieldLists.put("where_s", getAlternatives("where_s"));
// num_d name is historical, which is why we map it to num_f alternatives so
fieldLists.put("num_d", getAlternatives("num_f"));
// we can include floats as well
fieldLists.put("num_i", getAlternatives("num_i"));
fieldLists.put("super_s", getAlternatives("super_s"));
fieldLists.put("val_b", getAlternatives("val_b"));
fieldLists.put("date", getAlternatives("date_dt"));
fieldLists.put("sparse_s", getAlternatives("sparse_s"));
fieldLists.put("multi_ss", getAlternatives("multi_ss"));
int maxAlt = 0;
for (List<String> fieldList : fieldLists.values()) {
maxAlt = Math.max(fieldList.size(), maxAlt);
}
// take the field with the maximum number of alternative types and loop through our variants
// that many times
for (int i = 0; i < maxAlt; i++) {
ModifiableSolrParams args = params(p);
for (String field : fieldLists.keySet()) {
List<String> alts = fieldLists.get(field);
String alt = alts.get(i % alts.size());
args.add(field, alt);
}
args.set("rows", "0");
// doStatsTemplated(client, args);
}
// single valued strings
doStatsTemplated(
client,
params(
p,
"rows",
"0",
"noexist",
"noexist_s",
"cat_s",
"cat_s",
"where_s",
"where_s",
"num_d",
"num_d",
"num_i",
"num_i",
"num_l",
"long_l",
"super_s",
"super_s",
"val_b",
"val_b",
"date",
"date_dt",
"sparse_s",
"sparse_s",
"multi_ss",
"multi_ss"));
// multi-valued strings, long/float substitute for int/double
doStatsTemplated(
client,
params(
p,
"facet",
"true",
"rows",
"0",
"noexist",
"noexist_ss",
"cat_s",
"cat_ss",
"where_s",
"where_ss",
"num_d",
"num_f",
"num_i",
"num_l",
"num_l",
"long_l",
"num_is",
"num_ls",
"num_fs",
"num_ds",
"super_s",
"super_ss",
"val_b",
"val_b",
"date",
"date_dt",
"sparse_s",
"sparse_ss",
"multi_ss",
"multi_ss"));
// multi-valued strings, method=dv for terms facets
doStatsTemplated(
client,
params(
p,
"terms_method",
"method:dv,",
"rows",
"0",
"noexist",
"noexist_ss",
"cat_s",
"cat_ss",
"where_s",
"where_ss",
"num_d",
"num_f",
"num_i",
"num_l",
"num_l",
"long_l",
"super_s",
"super_ss",
"val_b",
"val_b",
"date",
"date_dt",
"sparse_s",
"sparse_ss",
"multi_ss",
"multi_ss"));
// single valued docvalues for strings, and single valued numeric doc values for numeric fields
doStatsTemplated(
client,
params(
p,
"rows",
"0",
"noexist",
"noexist_sd",
"cat_s",
"cat_sd",
"where_s",
"where_sd",
"num_d",
"num_dd",
"num_i",
"num_id",
"num_is",
"num_lds",
"num_l",
"long_ld",
"num_fs",
"num_dds",
"super_s",
"super_sd",
"val_b",
"val_b",
"date",
"date_dtd",
"sparse_s",
"sparse_sd",
"multi_ss",
"multi_sds"));
// multi-valued docvalues
FacetFieldProcessorByArrayDV.unwrap_singleValued_multiDv =
false; // better multi-valued coverage
doStatsTemplated(
client,
params(
p,
"rows",
"0",
"noexist",
"noexist_sds",
"cat_s",
"cat_sds",
"where_s",
"where_sds",
"num_d",
"num_d",
"num_i",
"num_i",
"num_is",
"num_ids",
"num_l",
"long_ld",
"num_fs",
"num_fds",
"super_s",
"super_sds",
"val_b",
"val_b",
"date",
"date_dtds",
"sparse_s",
"sparse_sds",
"multi_ss",
"multi_sds"));
// multi-valued docvalues
FacetFieldProcessorByArrayDV.unwrap_singleValued_multiDv = true;
doStatsTemplated(
client,
params(
p,
"rows",
"0",
"noexist",
"noexist_sds",
"cat_s",
"cat_sds",
"where_s",
"where_sds",
"num_d",
"num_d",
"num_i",
"num_i",
"num_is",
"num_ids",
"num_l",
"long_ld",
"num_fs",
"num_fds",
"super_s",
"super_sds",
"val_b",
"val_b",
"date",
"date_dtds",
"sparse_s",
"sparse_sds",
"multi_ss",
"multi_sds"));
}
public static void doStatsTemplated(Client client, ModifiableSolrParams p) throws Exception {
p.set("Z_num_i", "Z_" + p.get("num_i"));
p.set("Z_num_l", "Z_" + p.get("num_l"));
p.set("sparse_num_d", "sparse_" + p.get("num_d"));
if (p.get("num_is") == null) p.add("num_is", "num_is");
if (p.get("num_fs") == null) p.add("num_fs", "num_fs");
String terms = p.get("terms");
if (terms == null) terms = "";
int limit = 0;
switch (random().nextInt(4)) {
case 0:
limit = -1;
break;
case 1:
limit = 1000000;
break;
case 2: // fallthrough
case 3: // fallthrough
}
if (limit != 0) {
terms = terms + "limit:" + limit + ",";
}
String terms_method = p.get("terms_method");
if (terms_method != null) {
terms = terms + terms_method;
}
String refine_method = p.get("refine_method");
if (refine_method == null && random().nextBoolean()) {
refine_method = "refine:true,";
}
if (refine_method != null) terms = terms + refine_method;
p.set("terms", terms);
// "${terms}" should be put at the beginning of generic terms facets.
// It may specify "method=..." or "limit:-1", so should not be used if the facet explicitly
// specifies.
MacroExpander m = new MacroExpander(p.getMap());
String cat_s = m.expand("${cat_s}");
String where_s = m.expand("${where_s}");
String num_d = m.expand("${num_d}");
String num_i = m.expand("${num_i}");
String num_is = m.expand("${num_is}");
String num_fs = m.expand("${num_fs}");
String Z_num_i = m.expand("${Z_num_i}");
String Z_num_l = m.expand("${Z_num_l}");
String val_b = m.expand("${val_b}");
String date = m.expand("${date}");
String super_s = m.expand("${super_s}");
String sparse_s = m.expand("${sparse_s}");
String multi_ss = m.expand("${multi_ss}");
String sparse_num_d = m.expand("${sparse_num_d}");
client.deleteByQuery("*:*", null);
/* This code was not needed yet, but may be needed if we want to force empty shard results more often.
// create a new indexing client that doesn't use one shard to better test for empty or non-existent results
if (!client.local()) {
List<SolrClient> shards = client.getClientProvider().all();
iclient = new Client(shards.subList(0, shards.size()-1), client.getClientProvider().getSeed());
}
*/
SolrInputDocument doc =
sdoc(
"id",
"1",
cat_s,
"A",
where_s,
"NY",
num_d,
"4",
sparse_num_d,
"6",
num_i,
"2",
num_is,
"2",
num_is,
"-5",
num_fs,
"2",
num_fs,
"-5",
super_s,
"zodiac",
date,
"2001-01-01T01:01:01Z",
val_b,
"true",
sparse_s,
"one");
client.add(doc, null);
client.add(doc, null);
client.add(doc, null); // a couple of deleted docs
client.add(
sdoc(
"id",
"2",
cat_s,
"B",
where_s,
"NJ",
num_d,
"-9",
num_i,
"-5",
num_is,
"3",
num_is,
"-1",
num_fs,
"3",
num_fs,
"-1.5",
super_s,
"superman",
date,
"2002-02-02T02:02:02Z",
val_b,
"false",
multi_ss,
"a",
multi_ss,
"b",
Z_num_i,
"0",
Z_num_l,
"0"),
null);
client.add(sdoc("id", "3"), null);
client.commit();
client.add(
sdoc(
"id",
"4",
cat_s,
"A",
where_s,
"NJ",
num_d,
"2",
sparse_num_d,
"-4",
num_i,
"3",
num_is,
"0",
num_is,
"3",
num_fs,
"0",
num_fs,
"3",
super_s,
"spiderman",
date,
"2003-03-03T03:03:03Z",
multi_ss,
"b",
Z_num_i,
"" + Integer.MIN_VALUE,
Z_num_l,
Long.MIN_VALUE),
null);
client.add(
sdoc(
"id",
"5",
cat_s,
"B",
where_s,
"NJ",
num_d,
"11",
num_i,
"7",
num_is,
"0",
num_fs,
"0",
super_s,
"batman",
date,
"2001-02-03T01:02:03Z",
sparse_s,
"two",
multi_ss,
"a"),
null);
client.commit();
client.add(
sdoc(
"id",
"6",
cat_s,
"B",
where_s,
"NY",
num_d,
"-5",
num_i,
"-5",
num_is,
"-1",
num_fs,
"-1.5",
super_s,
"hulk",
date,
"2002-03-01T03:02:01Z",
multi_ss,
"b",
multi_ss,
"a",
Z_num_i,
"" + Integer.MAX_VALUE,
Z_num_l,
Long.MAX_VALUE),
null);
client.commit();
client.commit();
// test for presence of debugging info
ModifiableSolrParams debugP = params(p);
debugP.set("debugQuery", "true");
client.testJQ(
params(
debugP,
"q",
"*:*",
"json.facet",
"{catA:{query:{q:'${cat_s}:A'}}, catA2:{query:{query:'${cat_s}:A'}}, catA3:{query:'${cat_s}:A'} }"),
"facets=={ 'count':6, 'catA':{ 'count':2}, 'catA2':{ 'count':2}, 'catA3':{ 'count':2}}",
"debug/facet-trace==" // just test for presence, not exact structure / values
);
// straight query facets
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{catA:{query:{q:'${cat_s}:A'}}, catA2:{query:{query:'${cat_s}:A'}}, catA3:{query:'${cat_s}:A'} }"),
"facets=={ 'count':6, 'catA':{ 'count':2}, 'catA2':{ 'count':2}, 'catA3':{ 'count':2}}");
// nested query facets
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{ catB:{type:query, q:'${cat_s}:B', facet:{nj:{query:'${where_s}:NJ'}, ny:{query:'${where_s}:NY'}} }}"),
"facets=={ 'count':6, 'catB':{'count':3, 'nj':{'count':2}, 'ny':{'count':1}}}");
// nested query facets on subset
client.testJQ(
params(
p,
"q",
"id:(2 3)",
"json.facet",
"{ catB:{query:{q:'${cat_s}:B', facet:{nj:{query:'${where_s}:NJ'}, ny:{query:'${where_s}:NY'}} }}}"),
"facets=={ 'count':2, 'catB':{'count':1, 'nj':{'count':1}, 'ny':{'count':0}}}");
// nested query facets with stats
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{ catB:{query:{q:'${cat_s}:B', facet:{nj:{query:{q:'${where_s}:NJ'}}, ny:{query:'${where_s}:NY'}} }}}"),
"facets=={ 'count':6, 'catB':{'count':3, 'nj':{'count':2}, 'ny':{'count':1}}}");
// field/terms facet
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{c1:{field:'${cat_s}'}, c2:{field:{field:'${cat_s}'}}, c3:{${terms} type:terms, field:'${cat_s}'} }"),
"facets=={ 'count':6, "
+ "'c1':{ 'buckets':[{ 'val':'B', 'count':3}, { 'val':'A', 'count':2}]}, "
+ "'c2':{ 'buckets':[{ 'val':'B', 'count':3}, { 'val':'A', 'count':2}]}, "
+ "'c3':{ 'buckets':[{ 'val':'B', 'count':3}, { 'val':'A', 'count':2}]}} ");
// test mincount
client.testJQ(
params(p, "q", "*:*", "json.facet", "{f1:{terms:{${terms} field:'${cat_s}', mincount:3}}}"),
"facets=={ 'count':6, " + "'f1':{ 'buckets':[{ 'val':'B', 'count':3}]} } ");
// test default mincount of 1
client.testJQ(
params(p, "q", "id:1", "json.facet", "{f1:{terms:'${cat_s}'}}"),
"facets=={ 'count':1, " + "'f1':{ 'buckets':[{ 'val':'A', 'count':1}]} } ");
// test mincount of 0 - need processEmpty for distrib to match up
client.testJQ(
params(
p,
"q",
"id:1",
"json.facet",
"{processEmpty:true, f1:{terms:{${terms} field:'${cat_s}', mincount:0}}}"),
"facets=={ 'count':1, "
+ "'f1':{ 'buckets':[{ 'val':'A', 'count':1}, { 'val':'B', 'count':0}]} } ");
// test mincount of 0 with stats, need processEmpty for distrib to match up
client.testJQ(
params(
p,
"q",
"id:1",
"json.facet",
"{processEmpty:true, f1:{terms:{${terms} field:'${cat_s}', mincount:0, allBuckets:true, facet:{n1:'sum(${num_d})'} }}}"),
"facets=={ 'count':1, "
+ "'f1':{ allBuckets:{ 'count':1, n1:4.0}, 'buckets':[{ 'val':'A', 'count':1, n1:4.0}, { 'val':'B', 'count':0 /*, n1:0.0 */ }]} } ");
// test sorting by other stats
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms} field:'${cat_s}', sort:'n1 desc', facet:{n1:'sum(${num_d})'} }}"
+ " , f2:{terms:{${terms} field:'${cat_s}', sort:'n1 asc', facet:{n1:'sum(${num_d})'} }} }"),
"facets=={ 'count':6, "
+ " f1:{ 'buckets':[{ val:'A', count:2, n1:6.0 }, { val:'B', count:3, n1:-3.0}]}"
+ ", f2:{ 'buckets':[{ val:'B', count:3, n1:-3.0}, { val:'A', count:2, n1:6.0 }]} }");
// test trivial re-sorting by stats
// (there are other more in depth tests of this in doTestPrelimSorting, but this lets us check
// small responses with multiple templated params of diff real types)
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet", // num_d
"{f1:{terms:{${terms} field:'${cat_s}', "
+ " prelim_sort:'count desc', sort:'n1 desc', facet:{n1:'sum(${num_d})'} }},"
+ " f2:{terms:{${terms} field:'${cat_s}', "
+ " prelim_sort:'count asc', sort:'n1 asc', facet:{n1:'sum(${num_d})'} }} }"),
"facets=={ 'count':6 "
+ ", f1:{ 'buckets':[{ val:'A', count:2, n1:6.0 }, { val:'B', count:3, n1:-3.0}]}"
+ ", f2:{ 'buckets':[{ val:'B', count:3, n1:-3.0}, { val:'A', count:2, n1:6.0 }]} }");
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet", // num_i
"{f1:{terms:{${terms} field:'${cat_s}', "
+ " prelim_sort:'count desc', sort:'n1 desc', facet:{n1:'sum(${num_i})'} }},"
+ " f2:{terms:{${terms} field:'${cat_s}', "
+ " prelim_sort:'count asc', sort:'n1 asc', facet:{n1:'sum(${num_i})'} }} }"),
"facets=={ 'count':6 "
+ ", f1:{ 'buckets':[{ val:'A', count:2, n1:5.0 }, { val:'B', count:3, n1:-3.0}]}"
+ ", f2:{ 'buckets':[{ val:'B', count:3, n1:-3.0}, { val:'A', count:2, n1:5.0 }]} }");
// test sorting by other stats and more than one facet
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms} field:'${cat_s}', sort:'n1 desc', facet:{n1:'sum(${num_d})', n2:'avg(${num_d})'} }}"
+ " , f2:{terms:{${terms} field:'${cat_s}', sort:'n1 asc' , facet:{n1:'sum(${num_d})', n2:'avg(${num_d})'} }} }"),
"facets=={ 'count':6, "
+ " f1:{ 'buckets':[{ val:'A', count:2, n1:6.0 , n2:3.0 }, { val:'B', count:3, n1:-3.0, n2:-1.0}]}"
+ ", f2:{ 'buckets':[{ val:'B', count:3, n1:-3.0, n2:-1.0}, { val:'A', count:2, n1:6.0 , n2:3.0 }]} }");
// test sorting by other stats
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{${terms} type:terms, field:'${cat_s}', sort:'x desc', facet:{x:'min(${num_d})'} }"
+ " , f2:{${terms} type:terms, field:'${cat_s}', sort:'x desc', facet:{x:'max(${num_d})'} } "
+ " , f3:{${terms} type:terms, field:'${cat_s}', sort:'x desc', facet:{x:'unique(${where_s})'} } "
+ " , f4:{${terms} type:terms, field:'${cat_s}', sort:'x desc', facet:{x:'hll(${where_s})'} } "
+ " , f5:{${terms} type:terms, field:'${cat_s}', sort:'x desc', facet:{x:'variance(${num_d})'} } "
+ " , f6:{type:terms, field:${num_d}, limit:1, sort:'x desc', facet:{x:'hll(${num_i})'} } "
+ // facet on a field that will cause hashing and exercise hll.resize on numeric
// field
" , f7:{type:terms, field:${cat_s}, limit:2, sort:'x desc', facet:{x:'missing(${sparse_num_d})'} } "
+ " , f8:{type:terms, field:${cat_s}, limit:2, sort:'x desc', facet:{x:'countvals(${sparse_num_d})'} } "
+ "}"),
"facets=={ 'count':6, "
+ " f1:{ 'buckets':[{ val:'A', count:2, x:2.0 }, { val:'B', count:3, x:-9.0}]}"
+ ", f2:{ 'buckets':[{ val:'B', count:3, x:11.0 }, { val:'A', count:2, x:4.0 }]} "
+ ", f3:{ 'buckets':[{ val:'A', count:2, x:2 }, { val:'B', count:3, x:2 }]} "
+ ", f4:{ 'buckets':[{ val:'A', count:2, x:2 }, { val:'B', count:3, x:2 }]} "
+ ", f5:{ 'buckets':[{ val:'B', count:3, x:112.0 }, { val:'A', count:2, x:2.0 }]} "
+ ", f6:{ buckets:[{ val:-9.0, count:1, x:1 }]} "
+ ", f7:{ buckets:[{ val:B, count:3, x:3 },{ val:A, count:2, x:0 }]} "
+ ", f8:{ buckets:[{ val:A, count:2, x:2 },{ val:B, count:3, x:0 }]} "
+ "}");
// test for stdDev and variance of size 1 and 0
client.testJQ(
params(p, "q", "id:1", "json.facet", "{n1:'stddev(${num_d})', n2: 'variance(${num_d})'}"),
"facets=={ 'count':1, " + " n1:0.0, n2:0.0 }");
client.testJQ(
params(p, "q", "id:3", "json.facet", "{n1:'stddev(${num_d})', n2: 'variance(${num_d})'}"),
"facets=={ 'count':1, " + " n1:0.0, n2:0.0 }");
// test sorting by stat with function
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms} field:'${cat_s}', sort:'n1 desc', facet:{n1:'avg(add(${num_d},${num_d}))'} }}"
+ " , f2:{terms:{${terms} field:'${cat_s}', sort:'n1 asc', facet:{n1:'avg(add(${num_d},${num_d}))'} }} }"),
"facets=={ 'count':6, "
+ " f1:{ 'buckets':[{ val:'A', count:2, n1:6.0 }, { val:'B', count:3, n1:-2.0}]}"
+ ", f2:{ 'buckets':[{ val:'B', count:3, n1:-2.0}, { val:'A', count:2, n1:6.0 }]} }");
// test sorting by missing stat with function
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms} field:'${cat_s}', sort:'n1 desc', facet:{n1:'missing(field(${sparse_num_d}))'} }}"
+ " , f2:{terms:{${terms} field:'${cat_s}', sort:'n1 asc', facet:{n1:'missing(field(${sparse_num_d}))'} }} }"),
"facets=={ 'count':6, "
+ " f1:{ 'buckets':[{ val:'B', count:3, n1:3 }, { val:'A', count:2, n1:0}]}"
+ ", f2:{ 'buckets':[{ val:'A', count:2, n1:0}, { val:'B', count:3, n1:3 }]} }");
// test sorting by missing stat with domain query
client.testJQ(
params(
p,
"q",
"-id:*",
"json.facet",
"{f1:{terms:{${terms} field:'${cat_s}', domain:{query:'*:*'}, sort:'n1 desc', facet:{n1:'missing(field(${sparse_num_d}))'} }}"
+ " , f2:{terms:{${terms} field:'${cat_s}', domain:{query:'*:*'}, sort:'n1 asc', facet:{n1:'missing(field(${sparse_num_d}))'} }} }"),
"facets=={ 'count':0, "
+ " f1:{ 'buckets':[{ val:'B', count:3, n1:3 }, { val:'A', count:2, n1:0}]}"
+ ", f2:{ 'buckets':[{ val:'A', count:2, n1:0}, { val:'B', count:3, n1:3 }]} }");
// test with sub-facet aggregation with stat on field
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
" {f1:{terms:{${terms}, field:'${cat_s}', "
+ "facet:{f2:{terms:{${terms}, field:${where_s}, sort:'index asc', "
+ "facet:{n1:'missing(${sparse_num_d})'}}}}}}}"),
"facets=={ 'count':6, "
+ " f1:{ 'buckets':[{ val:'B', count:3, f2:{'buckets':[{val:'NJ', count:2, n1:2},{val:'NY', count:1, n1:1}]} },"
+ " { val:'A', count:2, f2:{'buckets':[{val:'NJ', count:1, n1:0},{val:'NY', count:1, n1:0}]}}]}"
+ "}");
// test with sub-facet aggregation with stat on func
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
" {f1:{terms:{${terms}, field:'${cat_s}', "
+ "facet:{f2:{terms:{${terms}, field:${where_s}, sort:'index asc', "
+ "facet:{n1:'missing(field(${sparse_num_d}))'}}}}}}}"),
"facets=={ 'count':6, "
+ " f1:{ 'buckets':[{ val:'B', count:3, f2:{'buckets':[{val:'NJ', count:2, n1:2},{val:'NY', count:1, n1:1}]} },"
+ " { val:'A', count:2, f2:{'buckets':[{val:'NJ', count:1, n1:0},{val:'NY', count:1, n1:0}]}}]}"
+ "}");
// test sorting by countvals stat with function
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms} field:'${cat_s}', sort:'n1 asc', facet:{n1:'countvals(field(${sparse_num_d}))'} }}"
+ " , f2:{terms:{${terms} field:'${cat_s}', sort:'n1 desc', facet:{n1:'countvals(field(${sparse_num_d}))'} }} }"),
"facets=={ 'count':6, "
+ " f1:{ 'buckets':[{ val:'B', count:3, n1:0 }, { val:'A', count:2, n1:2}]}"
+ ", f2:{ 'buckets':[{ val:'A', count:2, n1:2}, { val:'B', count:3, n1:0 }]} }");
// test sorting by countvals stat with domain query
client.testJQ(
params(
p,
"q",
"-id:*",
"json.facet",
"{f1:{terms:{${terms} field:'${cat_s}', domain:{query:'*:*'}, sort:'n1 asc', facet:{n1:'countvals(field(${sparse_num_d}))'} }}"
+ " , f2:{terms:{${terms} field:'${cat_s}', domain:{query:'*:*'}, sort:'n1 desc', facet:{n1:'countvals(field(${sparse_num_d}))'} }} }"),
"facets=={ 'count':0, "
+ " f1:{ 'buckets':[{ val:'B', count:3, n1:0 }, { val:'A', count:2, n1:2}]}"
+ ", f2:{ 'buckets':[{ val:'A', count:2, n1:2}, { val:'B', count:3, n1:0 }]} }");
// test with sub-facet aggregation with stat on field
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
" {f1:{terms:{${terms}, field:'${cat_s}', "
+ "facet:{f2:{terms:{${terms}, field:${where_s}, sort:'index asc', "
+ "facet:{n1:'countvals(${sparse_num_d})'}}}}}}}"),
"facets=={ 'count':6, "
+ " f1:{ 'buckets':[{ val:'B', count:3, f2:{'buckets':[{val:'NJ', count:2, n1:0},{val:'NY', count:1, n1:0}]} },"
+ " { val:'A', count:2, f2:{'buckets':[{val:'NJ', count:1, n1:1},{val:'NY', count:1, n1:1}]}}]}"
+ "}");
// test with sub-facet aggregation with stat on func
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
" {f1:{terms:{${terms}, field:'${cat_s}', "
+ "facet:{f2:{terms:{${terms}, field:${where_s}, sort:'index asc', "
+ "facet:{n1:'countvals(field(${sparse_num_d}))'}}}}}}}"),
"facets=={ 'count':6, "
+ " f1:{ 'buckets':[{ val:'B', count:3, f2:{'buckets':[{val:'NJ', count:2, n1:0},{val:'NY', count:1, n1:0}]} },"
+ " { val:'A', count:2, f2:{'buckets':[{val:'NJ', count:1, n1:1},{val:'NY', count:1, n1:1}]}}]}"
+ "}");
// facet on numbers to test resize from hashing (may need to be sorting by the metric to test
// that)
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{"
+ " f1:{${terms} type:field, field:${num_is}, facet:{a:'min(${num_i})'}, sort:'a asc' }"
+ ",f2:{${terms} type:field, field:${num_is}, facet:{a:'max(${num_i})'}, sort:'a desc' }"
+ "}"),
"facets=={count:6 "
+ ",f1:{ buckets:[{val:-1,count:2,a:-5},{val:3,count:2,a:-5},{val:-5,count:1,a:2},{val:2,count:1,a:2},{val:0,count:2,a:3} ] } "
+ ",f2:{ buckets:[{val:0,count:2,a:7},{val:3,count:2,a:3},{val:-5,count:1,a:2},{val:2,count:1,a:2},{val:-1,count:2,a:-5} ] } "
+ "}");
// Same thing for dates
// test min/max of string field
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{"
+ " f3:{${terms} type:field, field:${num_is}, facet:{a:'min(${date})'}, sort:'a desc' }"
+ ",f4:{${terms} type:field, field:${num_is}, facet:{a:'max(${date})'}, sort:'a asc' }"
+ "}"),
"facets=={count:6 "
+ ",f3:{ buckets:[{val:-1,count:2,a:'2002-02-02T02:02:02Z'},{val:3,count:2,a:'2002-02-02T02:02:02Z'},{val:0,count:2,a:'2001-02-03T01:02:03Z'},{val:-5,count:1,a:'2001-01-01T01:01:01Z'},{val:2,count:1,a:'2001-01-01T01:01:01Z'} ] } "
+ ",f4:{ buckets:[{val:-5,count:1,a:'2001-01-01T01:01:01Z'},{val:2,count:1,a:'2001-01-01T01:01:01Z'},{val:-1,count:2,a:'2002-03-01T03:02:01Z'},{val:0,count:2,a:'2003-03-03T03:03:03Z'},{val:3,count:2,a:'2003-03-03T03:03:03Z'} ] } "
+ "}");
// test field faceting on date field
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{"
+ " f1:{${terms} type:field, field:${date}}"
+ ",f2:{${terms} type:field, field:${date} sort:'index asc'}"
+ ",f3:{${terms} type:field, field:${date} sort:'index desc'}"
+
// ",f4:{${terms} type:field, field:${date},
// facet:{x:{type:field,field:${num_is},limit:1}} }" +
"}"),
"facets=={count:6 "
+ ",f1:{ buckets:[ {val:'2001-01-01T01:01:01Z', count:1},{val:'2001-02-03T01:02:03Z', count:1},{val:'2002-02-02T02:02:02Z', count:1},{val:'2002-03-01T03:02:01Z', count:1},{val:'2003-03-03T03:03:03Z', count:1} ] }"
+ ",f2:{ buckets:[ {val:'2001-01-01T01:01:01Z', count:1},{val:'2001-02-03T01:02:03Z', count:1},{val:'2002-02-02T02:02:02Z', count:1},{val:'2002-03-01T03:02:01Z', count:1},{val:'2003-03-03T03:03:03Z', count:1} ] }"
+ ",f3:{ buckets:[ {val:'2003-03-03T03:03:03Z', count:1},{val:'2002-03-01T03:02:01Z', count:1},{val:'2002-02-02T02:02:02Z', count:1},{val:'2001-02-03T01:02:03Z', count:1},{val:'2001-01-01T01:01:01Z', count:1} ] }"
+ "}");
// percentiles 0,10,50,90,100
// catA: 2.0 2.2 3.0 3.8 4.0
// catB: -9.0 -8.2 -5.0 7.800000000000001 11.0
// all: -9.0 -7.3999999999999995 2.0 8.200000000000001 11.0
// test sorting by single percentile
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms} field:'${cat_s}', sort:'n1 desc', facet:{n1:'percentile(${num_d},50)'} }}"
+ " , f2:{terms:{${terms} field:'${cat_s}', sort:'n1 asc', facet:{n1:'percentile(${num_d},50)'} }} "
+ " , f3:{terms:{${terms} field:'${cat_s}', sort:'n1 desc', facet:{n1:'percentile(${sparse_num_d},50)'} }} "
+ "}"),
"facets=={ 'count':6, "
+ " f1:{ 'buckets':[{ val:'A', count:2, n1:4.0 }, { val:'B', count:3, n1:-5.0}]}"
+ ", f2:{ 'buckets':[{ val:'B', count:3, n1:-5.0}, { val:'A', count:2, n1:4.0 }]}"
+ ", f3:{ 'buckets':[{ val:'A', count:2, n1:6.0}, { val:'B', count:3}]}"
+ "}");
// test sorting by multiple percentiles (sort is by first)
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms} field:${cat_s}, sort:'n1 desc', facet:{n1:'percentile(${num_d},50,0,100)'} }}"
+ " , f2:{terms:{${terms} field:${cat_s}, sort:'n1 asc', facet:{n1:'percentile(${num_d},50,0,100)'} }} }"),
"facets=={ 'count':6, "
+ " f1:{ 'buckets':[{ val:'A', count:2, n1:[4.0,2.0,4.0] }, { val:'B', count:3, n1:[-5.0,-9.0,11.0] }]}"
+ ", f2:{ 'buckets':[{ val:'B', count:3, n1:[-5.0,-9.0,11.0]}, { val:'A', count:2, n1:[4.0,2.0,4.0] }]} }");
// test sorting by count/index order
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms} field:'${cat_s}', sort:'count desc' } }"
+ " , f2:{terms:{${terms} field:'${cat_s}', sort:'count asc' } }"
+ " , f3:{terms:{${terms} field:'${cat_s}', sort:'index asc' } }"
+ " , f4:{terms:{${terms} field:'${cat_s}', sort:'index desc' } }"
+ "}"),
"facets=={ count:6 "
+ " ,f1:{buckets:[ {val:B,count:3}, {val:A,count:2} ] }"
+ " ,f2:{buckets:[ {val:A,count:2}, {val:B,count:3} ] }"
+ " ,f3:{buckets:[ {val:A,count:2}, {val:B,count:3} ] }"
+ " ,f4:{buckets:[ {val:B,count:3}, {val:A,count:2} ] }"
+ "}");
// test sorting by default count/index order
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms} field:'${cat_s}', sort:'count' } }"
+ " , f2:{terms:{${terms} field:'${cat_s}', sort:'count asc' } }"
+ " , f3:{terms:{${terms} field:'${cat_s}', sort:'index' } }"
+ " , f4:{terms:{${terms} field:'${cat_s}', sort:'index desc' } }"
+ "}"),
"facets=={ count:6 "
+ " ,f1:{buckets:[ {val:B,count:3}, {val:A,count:2} ] }"
+ " ,f2:{buckets:[ {val:A,count:2}, {val:B,count:3} ] }"
+ " ,f3:{buckets:[ {val:A,count:2}, {val:B,count:3} ] }"
+ " ,f4:{buckets:[ {val:B,count:3}, {val:A,count:2} ] }"
+ "}");
// test tiebreaks when sorting by count
client.testJQ(
params(
p,
"q",
"id:1 id:6",
"json.facet",
"{f1:{terms:{${terms} field:'${cat_s}', sort:'count desc' } }"
+ " , f2:{terms:{${terms} field:'${cat_s}', sort:'count asc' } }"
+ "}"),
"facets=={ count:2 "
+ " ,f1:{buckets:[ {val:A,count:1}, {val:B,count:1} ] }"
+ " ,f2:{buckets:[ {val:A,count:1}, {val:B,count:1} ] }"
+ "}");
// terms facet with nested query facet
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{cat:{terms:{${terms} field:'${cat_s}', facet:{nj:{query:'${where_s}:NJ'}} } }}"),
"facets=={ 'count':6, "
+ "'cat':{ 'buckets':[{ 'val':'B', 'count':3, 'nj':{ 'count':2}}, { 'val':'A', 'count':2, 'nj':{ 'count':1}}]} }");
// terms facet with nested query facet on subset
client.testJQ(
params(
p,
"q",
"id:(2 5 4)",
"json.facet",
"{cat:{terms:{${terms} field:'${cat_s}', facet:{nj:{query:'${where_s}:NJ'}} } }}"),
"facets=={ 'count':3, "
+ "'cat':{ 'buckets':[{ 'val':'B', 'count':2, 'nj':{ 'count':2}}, { 'val':'A', 'count':1, 'nj':{ 'count':1}}]} }");
// test prefix
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms} field:${super_s}, prefix:s, mincount:0 }}}" // even with
// mincount=0, we
// should only see
// buckets with the
// prefix
),
"facets=={ 'count':6, "
+ "'f1':{ 'buckets':[{val:spiderman, count:1}, {val:superman, count:1}]} } ");
// test prefix that doesn't exist
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms} field:${super_s}, prefix:ttt, mincount:0 }}}"),
"facets=={ 'count':6, " + "'f1':{ 'buckets':[]} } ");
// test prefix that doesn't exist at start
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms} field:${super_s}, prefix:aaaaaa, mincount:0 }}}"),
"facets=={ 'count':6, " + "'f1':{ 'buckets':[]} } ");
// test prefix that doesn't exist at end
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms} field:${super_s}, prefix:zzzzzz, mincount:0 }}}"),
"facets=={ 'count':6, " + "'f1':{ 'buckets':[]} } ");
// test prefix on where field
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{"
+ " f1:{${terms} type:terms, field:${where_s}, prefix:N }"
+ ",f2:{${terms} type:terms, field:${where_s}, prefix:NY }"
+ ",f3:{${terms} type:terms, field:${where_s}, prefix:NJ }"
+ "}"),
"facets=={ 'count':6 "
+ ",f1:{ 'buckets':[ {val:NJ,count:3}, {val:NY,count:2} ]}"
+ ",f2:{ 'buckets':[ {val:NY,count:2} ]}"
+ ",f3:{ 'buckets':[ {val:NJ,count:3} ]}"
+ " } ");
// test prefix on real multi-valued field
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{"
+ " f1:{${terms} type:terms, field:${multi_ss}, prefix:A }"
+ ",f2:{${terms} type:terms, field:${multi_ss}, prefix:z }"
+ ",f3:{${terms} type:terms, field:${multi_ss}, prefix:aa }"
+ ",f4:{${terms} type:terms, field:${multi_ss}, prefix:bb }"
+ ",f5:{${terms} type:terms, field:${multi_ss}, prefix:a }"
+ ",f6:{${terms} type:terms, field:${multi_ss}, prefix:b }"
+ "}"),
"facets=={ 'count':6 "
+ ",f1:{buckets:[]}"
+ ",f2:{buckets:[]}"
+ ",f3:{buckets:[]}"
+ ",f4:{buckets:[]}"
+ ",f5:{buckets:[ {val:a,count:3} ]}"
+ ",f6:{buckets:[ {val:b,count:3} ]}"
+ " } ");
//
// missing
//
// test missing w/ non-existent field
client.testJQ(
params(
p, "q", "*:*", "json.facet", "{f1:{terms:{${terms} field:${noexist}, missing:true}}}"),
"facets=={ 'count':6, " + "'f1':{ 'buckets':[], missing:{count:6} } } ");
// test missing
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms} field:${sparse_s}, missing:true }}}"),
"facets=={ 'count':6, "
+ "'f1':{ 'buckets':[{val:one, count:1}, {val:two, count:1}], missing:{count:4} } } ");
// test missing with stats
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms} field:${sparse_s}, missing:true, facet:{x:'sum(${num_d})'} }}}"),
"facets=={ 'count':6, "
+ "'f1':{ 'buckets':[{val:one, count:1, x:4.0}, {val:two, count:1, x:11.0}], missing:{count:4, x:-12.0} } } ");
// test that the missing bucket is not affected by any prefix
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms} field:${sparse_s}, missing:true, prefix:on, facet:{x:'sum(${num_d})'} }}}"),
"facets=={ 'count':6, "
+ "'f1':{ 'buckets':[{val:one, count:1, x:4.0}], missing:{count:4, x:-12.0} } } ");
// test missing with prefix that doesn't exist
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms} field:${sparse_s}, missing:true, prefix:ppp, facet:{x:'sum(${num_d})'} }}}"),
"facets=={ 'count':6, " + "'f1':{ 'buckets':[], missing:{count:4, x:-12.0} } } ");
// test numBuckets
client.testJQ(
params(
p,
"q",
"*:*",
"rows",
"0",
"facet",
"true",
"json.facet",
"{f1:{terms:{${terms_method} field:${cat_s}, numBuckets:true, limit:1}}}" // TODO:
// limit:0
// produced an
// error
),
"facets=={ 'count':6, " + "'f1':{ numBuckets:2, buckets:[{val:B, count:3}]} } ");
// prefix should lower numBuckets
client.testJQ(
params(
p,
"q",
"*:*",
"rows",
"0",
"facet",
"true",
"json.facet",
"{f1:{terms:{${terms} field:${cat_s}, numBuckets:true, prefix:B}}}"),
"facets=={ 'count':6, " + "'f1':{ numBuckets:1, buckets:[{val:B, count:3}]} } ");
// mincount should not lower numBuckets (since SOLR-10552)
client.testJQ(
params(
p,
"q",
"*:*",
"rows",
"0",
"facet",
"true",
"json.facet",
"{f1:{terms:{${terms} field:${cat_s}, numBuckets:true, mincount:3}}}"),
"facets=={ 'count':6, " + "'f1':{ numBuckets:2, buckets:[{val:B, count:3}]} } ");
// basic range facet
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f:{type:range, field:${num_d}, start:-5, end:10, gap:5}}"),
"facets=={count:6, f:{buckets:[ {val:-5.0,count:1}, {val:0.0,count:2}, {val:5.0,count:0} ] } }");
// basic range facet on dates
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f:{type:range, field:${date}, start:'2001-01-01T00:00:00Z', end:'2003-01-01T00:00:00Z', gap:'+1YEAR'}}"),
"facets=={count:6, f:{buckets:[ {val:'2001-01-01T00:00:00Z',count:2}, {val:'2002-01-01T00:00:00Z',count:2}] } }");
// range facet on dates w/ stats
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f:{type:range, field:${date}, start:'2002-01-01T00:00:00Z', end:'2005-01-01T00:00:00Z', gap:'+1YEAR', other:all, facet:{ x:'avg(${num_d})' } } }"),
"facets=={count:6, f:{buckets:[ {val:'2002-01-01T00:00:00Z',count:2,x:-7.0}, {val:'2003-01-01T00:00:00Z',count:1,x:2.0}, {val:'2004-01-01T00:00:00Z',count:0}], before:{count:2,x:7.5}, after:{count:0}, between:{count:3,x:-4.0} } }");
// basic range facet with "include" params
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f:{range:{field:${num_d}, start:-5, end:10, gap:5, include:upper}}}"),
"facets=={count:6, f:{buckets:[ {val:-5.0,count:0}, {val:0.0,count:2}, {val:5.0,count:0} ] } }");
// range facet with sub facets and stats
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f:{range:{field:${num_d}, start:-5, end:10, gap:5, facet:{ x:'sum(${num_i})', ny:{query:'${where_s}:NY'}} }}}"),
"facets=={count:6, f:{buckets:[ {val:-5.0,count:1,x:-5.0,ny:{count:1}}, {val:0.0,count:2,x:5.0,ny:{count:1}}, {val:5.0,count:0 /* ,x:0.0,ny:{count:0} */ } ] } }");
// range facet with sub facets and stats, with "other:all"
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f:{range:{field:${num_d}, start:-5, end:10, gap:5, other:all, facet:{ x:'sum(${num_i})', ny:{query:'${where_s}:NY'}} }}}"),
"facets=={count:6, f:{buckets:[ {val:-5.0,count:1,x:-5.0,ny:{count:1}}, {val:0.0,count:2,x:5.0,ny:{count:1}}, {val:5.0,count:0 /* ,x:0.0,ny:{count:0} */} ]"
+ ",before: {count:1,x:-5.0,ny:{count:0}}"
+ ",after: {count:1,x:7.0, ny:{count:0}}"
+ ",between:{count:3,x:0.0, ny:{count:2}}"
+ " } }");
// range facet with mincount
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f:{type:range, field:${num_d}, start:-5, end:10, gap:5, other:all, mincount:2, facet:{ x:'sum(${num_i})', ny:{query:'${where_s}:NY'}} }}"),
"facets=={count:6, f:{buckets:[ {val:0.0,count:2,x:5.0,ny:{count:1}} ]"
+ ",before: {count:1,x:-5.0,ny:{count:0}}"
+ ",after: {count:1,x:7.0, ny:{count:0}}"
+ ",between:{count:3,x:0.0, ny:{count:2}}"
+ " } }");
// sparse range facet (with sub facets and stats), with "other:all"
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f:{range:{field:${num_d}, start:-5, end:10, gap:1, other:all, "
+ " facet:{ x:'sum(${num_i})', ny:{query:'${where_s}:NY'}} }}}"),
"facets=={count:6, f:{buckets:[ {val:-5.0,count:1, x:-5.0,ny:{count:1}}, "
+ " {val:-4.0,count:0 /* ,x:0.0,ny:{count:0} */} ,"
+ " {val:-3.0,count:0 /* ,x:0.0,ny:{count:0} */} ,"
+ " {val:-2.0,count:0 /* ,x:0.0,ny:{count:0} */} ,"
+ " {val:-1.0,count:0 /* ,x:0.0,ny:{count:0} */} ,"
+ " {val: 0.0,count:0 /* ,x:0.0,ny:{count:0} */} ,"
+ " {val: 1.0,count:0 /* ,x:0.0,ny:{count:0} */} ,"
+ " {val: 2.0,count:1, x:3.0,ny:{count:0}} , "
+ " {val: 3.0,count:0 /* ,x:0.0,ny:{count:0} */} ,"
+ " {val: 4.0,count:1, x:2.0,ny:{count:1}} , "
+ " {val: 5.0,count:0 /* ,x:0.0,ny:{count:0} */} ,"
+ " {val: 6.0,count:0 /* ,x:0.0,ny:{count:0} */} ,"
+ " {val: 7.0,count:0 /* ,x:0.0,ny:{count:0} */} ,"
+ " {val: 8.0,count:0 /* ,x:0.0,ny:{count:0} */} ,"
+ " {val: 9.0,count:0 /* ,x:0.0,ny:{count:0} */}"
+ " ]"
+ " ,before: {count:1,x:-5.0,ny:{count:0}}"
+ " ,after: {count:1,x:7.0, ny:{count:0}}"
+ " ,between:{count:3,x:0.0, ny:{count:2}}"
+ " } }");
// sparse range facet (with sub facets and stats), with "other:all" & mincount==1
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f:{range:{field:${num_d}, start:-5, end:10, gap:1, other:all, mincount:1, "
+ " facet:{ x:'sum(${num_i})', ny:{query:'${where_s}:NY'}} }}}"),
"facets=={count:6, f:{buckets:[ {val:-5.0,count:1, x:-5.0,ny:{count:1}}, "
+ " {val: 2.0,count:1, x:3.0,ny:{count:0}} , "
+ " {val: 4.0,count:1, x:2.0,ny:{count:1}} "
+ " ]"
+ " ,before: {count:1,x:-5.0,ny:{count:0}}"
+ " ,after: {count:1,x:7.0, ny:{count:0}}"
+ " ,between:{count:3,x:0.0, ny:{count:2}}"
+ " } }");
// range facet with sub facets and stats, with "other:all", on subset
client.testJQ(
params(
p,
"q",
"id:(3 4 6)",
"json.facet",
"{f:{range:{field:${num_d}, start:-5, end:10, gap:5, other:all, facet:{ x:'sum(${num_i})', ny:{query:'${where_s}:NY'}} }}}"),
"facets=={count:3, f:{buckets:[ {val:-5.0,count:1,x:-5.0,ny:{count:1}}, {val:0.0,count:1,x:3.0,ny:{count:0}}, {val:5.0,count:0 /* ,x:0.0,ny:{count:0} */} ]"
+ ",before: {count:0 /* ,x:0.0,ny:{count:0} */ }"
+ ",after: {count:0 /* ,x:0.0,ny:{count:0} */}"
+ ",between:{count:2,x:-2.0, ny:{count:1}}"
+ " } }");
// range facet with stats on string fields
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f:{type:range, field:${num_d}, start:-5, end:10, gap:5, other:all, facet:{ wn:'unique(${where_s})',wh:'hll(${where_s})' } }}"),
"facets=={count:6, f:{buckets:[ {val:-5.0,count:1,wn:1,wh:1}, {val:0.0,count:2,wn:2,wh:2}, {val:5.0,count:0}]"
+ " ,before:{count:1,wn:1,wh:1}"
+ " ,after:{count:1,wn:1,wh:1} "
+ " ,between:{count:3,wn:2,wh:2} "
+ " } }");
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f:{type:range, field:${num_d}, start:-5, end:10, gap:5, other:all, facet:{ wmin:'min(${where_s})', wmax:'max(${where_s})' } }}"),
"facets=={count:6, f:{buckets:[ {val:-5.0,count:1,wmin:NY,wmax:NY}, {val:0.0,count:2,wmin:NJ,wmax:NY}, {val:5.0,count:0}]"
+ " ,before:{count:1,wmin:NJ,wmax:NJ}"
+ " ,after:{count:1,wmin:NJ,wmax:NJ} "
+ " ,between:{count:3,wmin:NJ,wmax:NY} "
+ " } }");
// stats at top level
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{ sum1:'sum(${num_d})', sumsq1:'sumsq(${num_d})', avg1:'avg(${num_d})', avg2:'avg(def(${num_d},0))', mind:'min(${num_d})', maxd:'max(${num_d})'"
+ ", numwhere:'unique(${where_s})', unique_num_i:'unique(${num_i})', unique_num_d:'unique(${num_d})', unique_date:'unique(${date})'"
+ ", where_hll:'hll(${where_s})', hll_num_i:'hll(${num_i})', hll_num_d:'hll(${num_d})', hll_date:'hll(${date})'"
+ ", med:'percentile(${num_d},50)', perc:'percentile(${num_d},0,50.0,100)', variance:'variance(${num_d})', stddev:'stddev(${num_d})'"
+ ", mini:'min(${num_i})', maxi:'max(${num_i})', missing:'missing(${sparse_num_d})', vals:'countvals(${sparse_num_d})'"
+ " }"),
"facets=={ 'count':6, "
+ "sum1:3.0, sumsq1:247.0, avg1:0.6, avg2:0.5, mind:-9.0, maxd:11.0"
+ ", numwhere:2, unique_num_i:4, unique_num_d:5, unique_date:5"
+ ", where_hll:2, hll_num_i:4, hll_num_d:5, hll_date:5"
+ ", med:2.0, perc:[-9.0,2.0,11.0], variance:61.3, stddev:7.829431652425353"
+ ", mini:-5, maxi:7, missing:4, vals:2"
+ "}");
// stats at top level on multi-valued fields
client.testJQ(
params(
p,
"q",
"*:*",
"myfield",
"${multi_ss}",
"json.facet",
"{ sum1:'sum(${num_fs})', sumsq1:'sumsq(${num_fs})', avg1:'avg(${num_fs})', mind:'min(${num_fs})', maxd:'max(${num_fs})'"
+ ", mini:'min(${num_is})', maxi:'max(${num_is})', mins:'min(${multi_ss})', maxs:'max(${multi_ss})'"
+ ", stddev:'stddev(${num_fs})', variance:'variance(${num_fs})', median:'percentile(${num_fs}, 50)'"
+ ", perc:'percentile(${num_fs}, 0,75,100)', maxss:'max($multi_ss)'"
+ " }"),
"facets=={ 'count':6, "
+ "sum1:0.0, sumsq1:51.5, avg1:0.0, mind:-5.0, maxd:3.0"
+ ", mini:-5, maxi:3, mins:'a', maxs:'b'"
+ ", stddev:2.712405363721075, variance:7.3571428571, median:0.0, perc:[-5.0,3.0,3.0], maxss:'b'"
+ "}");
// test sorting by multi-valued
client.testJQ(
params(
p,
"q",
"*:*",
"my_field",
"${num_is}",
"json.facet",
"{f1:{terms:{${terms} field:'${cat_s}', sort:'n1 desc', facet:{n1:'avg($my_field)'} }}"
+ " , f2:{terms:{${terms} field:'${cat_s}', sort:'n1 asc', facet:{n1:'avg($my_field)'} }} }"),
"facets=={ 'count':6, "
+ " f1:{ 'buckets':[{ val:'B', count:3, n1: 0.25}, { val:'A', count:2, n1:0.0}]}"
+ ", f2:{ 'buckets':[{ val:'A', count:2, n1:0.0}, { val:'B', count:3, n1:0.25 }]} }");
// test sorting by percentile
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms} field:'${cat_s}', sort:'n1 asc', facet:{n1:'percentile(${num_is}, 50)'} }}"
+ " , f2:{terms:{${terms} field:'${cat_s}', sort:'n1 desc', facet:{n1:'percentile(${num_is}, 50)'} }} }"),
"facets=={ 'count':6, "
+ " f1:{ 'buckets':[{ val:'B', count:3, n1: 0.0}, { val:'A', count:2, n1:2.0}]}"
+ ", f2:{ 'buckets':[{ val:'A', count:2, n1:2.0}, { val:'B', count:3, n1:0.0 }]} }");
// test sorting by multi-valued field with domain query
client.testJQ(
params(
p,
"q",
"-id:*",
"json.facet",
"{f1:{terms:{${terms} field:'${cat_s}', domain:{query:'*:*'}, sort:'n1 desc', facet:{n1:'sum(${num_is})'} }}"
+ " , f2:{terms:{${terms} field:'${cat_s}', domain:{query:'*:*'}, sort:'n1 asc', facet:{n1:'sum(${num_is})'} }} }"),
"facets=={ 'count':0, "
+ " f1:{ 'buckets':[{ val:'B', count:3, n1:1.0 }, { val:'A', count:2, n1:0.0}]}"
+ ", f2:{ 'buckets':[{ val:'A', count:2, n1:0.0}, { val:'B', count:3, n1:1.0 }]} }");
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
" {f1:{terms:{${terms}, field:'${cat_s}', "
+ "facet:{f2:{terms:{${terms}, field:${where_s}, sort:'index asc', "
+ "facet:{n1:'min(${multi_ss})'}}}}}}}"),
"facets=={ 'count':6, "
+ " f1:{ 'buckets':[{ val:'B', count:3, f2:{'buckets':[{val:'NJ', count:2, n1:'a'},{val:'NY', count:1, n1:'a'}]} },"
+ " { val:'A', count:2, f2:{'buckets':[{val:'NJ', count:1, n1:'b'},{val:'NY', count:1}]}}]}"
+ "}");
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
" {f1:{terms:{${terms}, field:'${cat_s}', "
+ "facet:{f2:{terms:{${terms}, field:${where_s}, sort:'index asc', "
+ "facet:{n1:'max(${multi_ss})'}}}}}}}"),
"facets=={ 'count':6, "
+ " f1:{ 'buckets':[{ val:'B', count:3, f2:{'buckets':[{val:'NJ', count:2, n1:'b'},{val:'NY', count:1, n1:'b'}]} },"
+ " { val:'A', count:2, f2:{'buckets':[{val:'NJ', count:1, n1:'b'},{val:'NY', count:1}]}}]}"
+ "}");
// stats at top level, no matches
client.testJQ(
params(
p,
"q",
"id:DOESNOTEXIST",
"json.facet",
"{ sum1:'sum(${num_d})', sumsq1:'sumsq(${num_d})', avg1:'avg(${num_d})', min1:'min(${num_d})', max1:'max(${num_d})'"
+ ", numwhere:'unique(${where_s})', unique_num_i:'unique(${num_i})', unique_num_d:'unique(${num_d})', unique_date:'unique(${date})'"
+ ", where_hll:'hll(${where_s})', hll_num_i:'hll(${num_i})', hll_num_d:'hll(${num_d})', hll_date:'hll(${date})'"
+ ", med:'percentile(${num_d},50)', perc:'percentile(${num_d},0,50.0,100)', variance:'variance(${num_d})', stddev:'stddev(${num_d})' }"),
"facets=={count:0 "
+ "\n// ,sum1:0.0, sumsq1:0.0, avg1:0.0, min1:'NaN', max1:'NaN', numwhere:0 \n"
+ " }");
// stats at top level, matching documents, but no values in the field
// NOTE: this represents the current state of what is returned, not the ultimate desired state.
client.testJQ(
params(
p,
"q",
"id:3",
"json.facet",
"{ sum1:'sum(${num_d})', sumsq1:'sumsq(${num_d})', avg1:'avg(${num_d})', min1:'min(${num_d})', max1:'max(${num_d})'"
+ ", numwhere:'unique(${where_s})', unique_num_i:'unique(${num_i})', unique_num_d:'unique(${num_d})', unique_date:'unique(${date})'"
+ ", where_hll:'hll(${where_s})', hll_num_i:'hll(${num_i})', hll_num_d:'hll(${num_d})', hll_date:'hll(${date})'"
+ ", med:'percentile(${num_d},50)', perc:'percentile(${num_d},0,50.0,100)', variance:'variance(${num_d})', stddev:'stddev(${num_d})' }"),
"facets=={count:1 "
+ ",sum1:0.0,"
+ " sumsq1:0.0,"
+ " avg1:0.0,"
+ // TODO: undesirable. omit?
// " min1:'NaN'," +
// " max1:'NaN'," +
" numwhere:0,"
+ " unique_num_i:0,"
+ " unique_num_d:0,"
+ " unique_date:0,"
+ " where_hll:0,"
+ " hll_num_i:0,"
+ " hll_num_d:0,"
+ " hll_date:0,"
+ " variance:0.0,"
+ " stddev:0.0"
+ " }");
//
// tests on a multi-valued field with actual multiple values, just to ensure that we are
// using a multi-valued method for the rest of the tests when appropriate.
//
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{cat:{terms:{${terms} field:'${multi_ss}', facet:{nj:{query:'${where_s}:NJ'}} } }}"),
"facets=={ 'count':6, "
+ "'cat':{ 'buckets':[{ 'val':'a', 'count':3, 'nj':{ 'count':2}}, { 'val':'b', 'count':3, 'nj':{ 'count':2}}]} }");
// test unique on multi-valued field
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{"
+ "x:'unique(${multi_ss})'"
+ ",z:'missing(${multi_ss})'"
+ ",z1:'missing(${num_is})'"
+ ",v:'countvals(${multi_ss})'"
+ ",v1:'countvals(${num_is})'"
+ ",y:{query:{q:'id:2', facet:{x:'unique(${multi_ss})'} }} "
+ ",x2:'hll(${multi_ss})'"
+ ",y2:{query:{q:'id:2', facet:{x:'hll(${multi_ss})'} }} "
+ " }"),
"facets=={count:6 "
+ ",x:2"
+ ",z:2"
+ ",z1:1"
+ ",v:6"
+ ",v1:8"
+ ",y:{count:1, x:2}"
+ // single document should yield 2 unique values
",x2:2"
+ ",y2:{count:1, x:2}"
+ // single document should yield 2 unique values
" }");
// test allBucket multi-valued
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{x:{terms:{${terms} field:'${multi_ss}',allBuckets:true}}}"),
"facets=={ count:6, "
+ "x:{ buckets:[{val:a, count:3}, {val:b, count:3}] , allBuckets:{count:6} } }");
// allBuckets for multi-valued field with stats. This can sometimes take a different path of
// adding complete DocSets to the Acc
// also test limit:0
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{"
+ " f0:{${terms_method} type:terms, field:${multi_ss}, allBuckets:true, limit:0} "
+ ",f1:{${terms_method} type:terms, field:${multi_ss}, allBuckets:true, limit:0, offset:1} "
+ // offset with 0 limit
",f2:{${terms_method} type:terms, field:${multi_ss}, allBuckets:true, limit:0, facet:{x:'sum(${num_d})'}, sort:'x desc' } "
+ ",f3:{${terms_method} type:terms, field:${multi_ss}, allBuckets:true, limit:0, missing:true, facet:{x:'sum(${num_d})', y:'avg(${num_d})'}, sort:'x desc' } "
+ "}"),
"facets=={ 'count':6, "
+ " f0:{allBuckets:{count:6}, buckets:[]}"
+ ",f1:{allBuckets:{count:6}, buckets:[]}"
+ ",f2:{allBuckets:{count:6, x:-15.0}, buckets:[]} "
+ ",f3:{allBuckets:{count:6, x:-15.0, y:-2.5}, buckets:[], missing:{count:2, x:4.0, y:4.0} }} "
+ "}");
// allBuckets with numeric field with stats.
// also test limit:0
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{"
+ " f0:{${terms_method} type:terms, field:${num_i}, allBuckets:true, limit:0} "
+ ",f1:{${terms_method} type:terms, field:${num_i}, allBuckets:true, limit:0, offset:1} "
+ // offset with 0 limit
",f2:{${terms_method} type:terms, field:${num_i}, allBuckets:true, limit:0, facet:{x:'sum(${num_d})'}, sort:'x desc' } "
+ "}"),
"facets=={ 'count':6, "
+ " f0:{allBuckets:{count:5}, buckets:[]}"
+ ",f1:{allBuckets:{count:5}, buckets:[]}"
+ ",f2:{allBuckets:{count:5, x:3.0}, buckets:[]} "
+ "}");
//////////////////////////////////////////////////////////////////////////////////////////////////////////
// test converting legacy facets
// test mincount
client.testJQ(
params(
p,
"q",
"*:*",
// , "json.facet", "{f1:{terms:{field:'${cat_s}', mincount:3}}}"
"facet",
"true",
"facet.version",
"2",
"facet.field",
"{!key=f1}${cat_s}",
"facet.mincount",
"3"),
"facets=={ 'count':6, " + "'f1':{ 'buckets':[{ 'val':'B', 'count':3}]} } ");
// test prefix
client.testJQ(
params(
p,
"q",
"*:*",
// , "json.facet", "{f1:{terms:{field:${super_s}, prefix:s, mincount:0 }}}" // even
// with mincount=0, we should only see buckets with the prefix
"facet",
"true",
"facet.version",
"2",
"facet.field",
"{!key=f1}${super_s}",
"facet.prefix",
"s",
"facet.mincount",
"0"),
"facets=={ 'count':6, "
+ "'f1':{ 'buckets':[{val:spiderman, count:1}, {val:superman, count:1}]} } ");
// range facet with sub facets and stats
client.testJQ(
params(
p,
"q",
"*:*",
// , "json.facet", "{f:{range:{field:${num_d}, start:-5, end:10, gap:5, facet:{
// x:'sum(${num_i})', ny:{query:'${where_s}:NY'}} }}}"
"facet",
"true",
"facet.version",
"2",
"facet.range",
"{!key=f}${num_d}",
"facet.range.start",
"-5",
"facet.range.end",
"10",
"facet.range.gap",
"5",
"f.f.facet.stat",
"x:sum(${num_i})",
"subfacet.f.query",
"{!key=ny}${where_s}:NY"),
"facets=={count:6, f:{buckets:[ {val:-5.0,count:1,x:-5.0,ny:{count:1}}, {val:0.0,count:2,x:5.0,ny:{count:1}}, {val:5.0,count:0 /* ,x:0.0,ny:{count:0} */ } ] } }");
// test sorting by stat
client.testJQ(
params(
p,
"q",
"*:*",
// , "json.facet", "{f1:{terms:{field:'${cat_s}', sort:'n1 desc',
// facet:{n1:'sum(${num_d})'} }}" +
// " , f2:{terms:{field:'${cat_s}', sort:'n1 asc', facet:{n1:'sum(${num_d})'} }} }"
"facet",
"true",
"facet.version",
"2",
"facet.field",
"{!key=f1}${cat_s}",
"f.f1.facet.sort",
"n1 desc",
"facet.stat",
"n1:sum(${num_d})",
"facet.field",
"{!key=f2}${cat_s}",
"f.f1.facet.sort",
"n1 asc"),
"facets=={ 'count':6, "
+ " f1:{ 'buckets':[{ val:'A', count:2, n1:6.0 }, { val:'B', count:3, n1:-3.0}]}"
+ ", f2:{ 'buckets':[{ val:'B', count:3, n1:-3.0}, { val:'A', count:2, n1:6.0 }]} }");
// range facet with sub facets and stats, with "other:all", on subset
client.testJQ(
params(
p,
"q",
"id:(3 4 6)",
// , "json.facet", "{f:{range:{field:${num_d}, start:-5, end:10, gap:5, other:all,
// facet:{ x:'sum(${num_i})', ny:{query:'${where_s}:NY'}} }}}"
"facet",
"true",
"facet.version",
"2",
"facet.range",
"{!key=f}${num_d}",
"facet.range.start",
"-5",
"facet.range.end",
"10",
"facet.range.gap",
"5",
"f.f.facet.stat",
"x:sum(${num_i})",
"subfacet.f.query",
"{!key=ny}${where_s}:NY",
"facet.range.other",
"all"),
"facets=={count:3, f:{buckets:[ {val:-5.0,count:1,x:-5.0,ny:{count:1}}, {val:0.0,count:1,x:3.0,ny:{count:0}}, {val:5.0,count:0 /* ,x:0.0,ny:{count:0} */} ]"
+ ",before: {count:0 /* ,x:0.0,ny:{count:0} */ }"
+ ",after: {count:0 /* ,x:0.0,ny:{count:0} */}"
+ ",between:{count:2,x:-2.0, ny:{count:1}}"
+ " } }");
////////////////////////////////////////////////////////////////////////////////////////////
// multi-select / exclude tagged filters via excludeTags
////////////////////////////////////////////////////////////////////////////////////////////
// test uncached multi-select (see SOLR-8496)
client.testJQ(
params(
p,
"q",
"{!cache=false}*:*",
"fq",
"{!tag=doc3,allfilt}-id:3",
"json.facet",
"{" + "f1:{${terms} type:terms, field:${cat_s}, domain:{excludeTags:doc3} } " + "}"),
"facets=={ count:5, " + " f1:{ buckets:[ {val:B, count:3}, {val:A, count:2} ] }" + "}");
// test sub-facets of empty buckets with domain filter exclusions (canProduceFromEmpty) (see
// SOLR-9519)
client.testJQ(
params(
p,
"q",
"*:*",
"fq",
"{!tag=doc3}id:non-exist",
"fq",
"{!tag=CATA}${cat_s}:A",
"json.facet",
"{"
+ "f1:{${terms} type:terms, field:${cat_s}, domain:{excludeTags:doc3} } "
+ ",q1 :{type:query, q:'*:*', facet:{ f1:{${terms} type:terms, field:${cat_s}, domain:{excludeTags:doc3} } } } "
+ // nested under query
",q1a:{type:query, q:'id:4', facet:{ f1:{${terms} type:terms, field:${cat_s}, domain:{excludeTags:doc3} } } } "
+ // nested under query, make sure id:4 filter still applies
",r1 :{type:range, field:${num_d}, start:0, gap:3, end:5, facet:{ f1:{${terms} type:terms, field:${cat_s}, domain:{excludeTags:doc3} } } } "
+ // nested under range, make sure range constraints still apply
",f2:{${terms} type:terms, field:${cat_s}, domain:{filter:'*:*'} } "
+ // domain filter doesn't widen, so f2 should not appear.
"}"),
"facets=={ count:0, "
+ " f1:{ buckets:[ {val:A, count:2} ] }"
+ ",q1:{ count:0, f1:{buckets:[{val:A, count:2}]} }"
+ ",q1a:{ count:0, f1:{buckets:[{val:A, count:1}]} }"
+ ",r1:{ buckets:[ {val:0.0,count:0,f1:{buckets:[{val:A, count:1}]}}, {val:3.0,count:0,f1:{buckets:[{val:A, count:1}]}} ] }"
+ "}");
// nested query facets on subset (with excludeTags)
client.testJQ(
params(
p,
"q",
"*:*",
"fq",
"{!tag=abc}id:(2 3)",
"json.facet",
"{ processEmpty:true,"
+ " f1:{query:{q:'${cat_s}:B', facet:{nj:{query:'${where_s}:NJ'}, ny:{query:'${where_s}:NY'}} , excludeTags:[xyz,qaz]}}"
+ ",f2:{query:{q:'${cat_s}:B', facet:{nj:{query:'${where_s}:NJ'}, ny:{query:'${where_s}:NY'}} , excludeTags:abc }}"
+ ",f3:{query:{q:'${cat_s}:B', facet:{nj:{query:'${where_s}:NJ'}, ny:{query:'${where_s}:NY'}} , excludeTags:'xyz ,abc ,qaz' }}"
+ ",f4:{query:{q:'${cat_s}:B', facet:{nj:{query:'${where_s}:NJ'}, ny:{query:'${where_s}:NY'}} , excludeTags:[xyz , abc , qaz] }}"
+ ",f5:{query:{q:'${cat_s}:B', facet:{nj:{query:'${where_s}:NJ'}, ny:{query:'${where_s}:NY'}} , excludeTags:[xyz,qaz]}}"
+ // this is repeated, but it did fail when a single context was shared among
// sub-facets
",f6:{query:{q:'${cat_s}:B', facet:{processEmpty:true, nj:{query:'${where_s}:NJ'}, ny:{ type:query, q:'${where_s}:NY', excludeTags:abc}} }}"
+ // exclude in a sub-facet
",f7:{query:{q:'${cat_s}:B', facet:{processEmpty:true, nj:{query:'${where_s}:NJ'}, ny:{ type:query, q:'${where_s}:NY', excludeTags:xyz}} }}"
+ // exclude in a sub-facet that doesn't match
"}"),
"facets=={ 'count':2, "
+ " 'f1':{'count':1, 'nj':{'count':1}, 'ny':{'count':0}}"
+ ",'f2':{'count':3, 'nj':{'count':2}, 'ny':{'count':1}}"
+ ",'f3':{'count':3, 'nj':{'count':2}, 'ny':{'count':1}}"
+ ",'f4':{'count':3, 'nj':{'count':2}, 'ny':{'count':1}}"
+ ",'f5':{'count':1, 'nj':{'count':1}, 'ny':{'count':0}}"
+ ",'f6':{'count':1, 'nj':{'count':1}, 'ny':{'count':1}}"
+ ",'f7':{'count':1, 'nj':{'count':1}, 'ny':{'count':0}}"
+ "}");
// terms facet with nested query facet (with excludeTags, using new format inside domain:{})
client.testJQ(
params(
p,
"q",
"{!cache=false}*:*",
"fq",
"{!tag=doc6,allfilt}-id:6",
"fq",
"{!tag=doc3,allfilt}-id:3",
"json.facet",
"{processEmpty:true, "
+ " f0:{${terms} type:terms, field:${cat_s}, facet:{nj:{query:'${where_s}:NJ'}} } "
+ ",f1:{${terms} type:terms, field:${cat_s}, domain:{excludeTags:doc3}, missing:true, facet:{nj:{query:'${where_s}:NJ'}} } "
+ ",f2:{${terms} type:terms, field:${cat_s}, domain:{excludeTags:allfilt},missing:true, facet:{nj:{query:'${where_s}:NJ'}} } "
+ ",f3:{${terms} type:terms, field:${cat_s}, domain:{excludeTags:doc6}, missing:true, facet:{nj:{query:'${where_s}:NJ'}} } "
+ "}"),
"facets=={ count:4, "
+ " f0:{ buckets:[ {val:A, count:2, nj:{ count:1}}, {val:B, count:2, nj:{count:2}} ] }"
+ ",f1:{ buckets:[ {val:A, count:2, nj:{ count:1}}, {val:B, count:2, nj:{count:2}} ] , missing:{count:1,nj:{count:0}} }"
+ ",f2:{ buckets:[ {val:B, count:3, nj:{ count:2}}, {val:A, count:2, nj:{count:1}} ] , missing:{count:1,nj:{count:0}} }"
+ ",f3:{ buckets:[ {val:B, count:3, nj:{ count:2}}, {val:A, count:2, nj:{count:1}} ] , missing:{count:0} }"
+ "}");
// range facet with sub facets and stats, with "other:all" (with excludeTags)
client.testJQ(
params(
p,
"q",
"*:*",
"fq",
"{!tag=doc6,allfilt}-id:6",
"fq",
"{!tag=doc3,allfilt}-id:3",
"json.facet",
"{processEmpty:true "
+ ", f1:{type:range, field:${num_d}, start:-5, end:10, gap:5, other:all, facet:{ x:'sum(${num_i})', ny:{query:'${where_s}:NY'}} , domain:{excludeTags:allfilt} }"
+ ", f2:{type:range, field:${num_d}, start:-5, end:10, gap:5, other:all, facet:{ x:'sum(${num_i})', ny:{query:'${where_s}:NY'}} }"
+ "}"),
"facets=={count:4"
+ ",f1:{buckets:[ {val:-5.0,count:1,x:-5.0,ny:{count:1}}, {val:0.0,count:2,x:5.0,ny:{count:1}}, {val:5.0,count:0} ]"
+ ",before: {count:1,x:-5.0,ny:{count:0}}"
+ ",after: {count:1,x:7.0, ny:{count:0}}"
+ ",between:{count:3,x:0.0, ny:{count:2}} }"
+ ",f2:{buckets:[ {val:-5.0,count:0}, {val:0.0,count:2,x:5.0,ny:{count:1}}, {val:5.0,count:0} ]"
+ ",before: {count:1,x:-5.0,ny:{count:0}}"
+ ",after: {count:1,x:7.0, ny:{count:0}}"
+ ",between:{count:2,x:5.0, ny:{count:1}} }"
+ "}");
//
// facet on numbers
//
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{"
+ " f1:{${terms} type:field, field:${num_i} }"
+ ",f2:{${terms} type:field, field:${num_i}, sort:'count asc' }"
+ ",f3:{${terms} type:field, field:${num_i}, sort:'index asc' }"
+ ",f4:{${terms} type:field, field:${num_i}, sort:'index desc' }"
+ ",f5:{${terms} type:field, field:${num_i}, sort:'index desc', limit:1, missing:true, allBuckets:true, numBuckets:true }"
+ ",f6:{${terms} type:field, field:${num_i}, sort:'index desc', mincount:2, numBuckets:true }"
+ // mincount should not lower numbuckets (since SOLR-10552)
",f7:{${terms} type:field, field:${num_i}, sort:'index desc', offset:2, numBuckets:true }"
+ // test offset
",f8:{${terms} type:field, field:${num_i}, sort:'index desc', offset:100, numBuckets:true }"
+ // test high offset
",f9:{${terms} type:field, field:${num_i}, sort:'x desc', facet:{x:'avg(${num_d})'}, missing:true, allBuckets:true, numBuckets:true }"
+ // test stats
",f10:{${terms} type:field, field:${num_i}, facet:{a:{query:'${cat_s}:A'}}, missing:true, allBuckets:true, numBuckets:true }"
+ // test subfacets
",f11:{${terms} type:field, field:${num_i}, facet:{a:'unique(${num_d})'} ,missing:true, allBuckets:true, sort:'a desc' }"
+ // test subfacet using unique on numeric field (this previously triggered a
// resizing bug)
"}"),
"facets=={count:6 "
+ ",f1:{ buckets:[{val:-5,count:2},{val:2,count:1},{val:3,count:1},{val:7,count:1} ] } "
+ ",f2:{ buckets:[{val:2,count:1},{val:3,count:1},{val:7,count:1},{val:-5,count:2} ] } "
+ ",f3:{ buckets:[{val:-5,count:2},{val:2,count:1},{val:3,count:1},{val:7,count:1} ] } "
+ ",f4:{ buckets:[{val:7,count:1},{val:3,count:1},{val:2,count:1},{val:-5,count:2} ] } "
+ ",f5:{ buckets:[{val:7,count:1}] , numBuckets:4, allBuckets:{count:5}, missing:{count:1} } "
+ ",f6:{ buckets:[{val:-5,count:2}] , numBuckets:4 } "
+ ",f7:{ buckets:[{val:2,count:1},{val:-5,count:2}] , numBuckets:4 } "
+ ",f8:{ buckets:[] , numBuckets:4 } "
+ ",f9:{ buckets:[{val:7,count:1,x:11.0},{val:2,count:1,x:4.0},{val:3,count:1,x:2.0},{val:-5,count:2,x:-7.0} ], numBuckets:4, allBuckets:{count:5,x:0.6},missing:{count:1,x:0.0} } "
+ // TODO: should missing exclude "x" because no values were collected?
",f10:{ buckets:[{val:-5,count:2,a:{count:0}},{val:2,count:1,a:{count:1}},{val:3,count:1,a:{count:1}},{val:7,count:1,a:{count:0}} ], numBuckets:4, allBuckets:{count:5},missing:{count:1,a:{count:0}} } "
+ ",f11:{ buckets:[{val:-5,count:2,a:2},{val:2,count:1,a:1},{val:3,count:1,a:1},{val:7,count:1,a:1} ] , missing:{count:1,a:0} , allBuckets:{count:5,a:5} } "
+ "}");
// facet on a float field - shares same code with integers/longs currently, so we only need to
// test labels/sorting
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{"
+ " f1:{${terms} type:field, field:${num_d} }"
+ ",f2:{${terms} type:field, field:${num_d}, sort:'index desc' }"
+ "}"),
"facets=={count:6 "
+ ",f1:{ buckets:[{val:-9.0,count:1},{val:-5.0,count:1},{val:2.0,count:1},{val:4.0,count:1},{val:11.0,count:1} ] } "
+ ",f2:{ buckets:[{val:11.0,count:1},{val:4.0,count:1},{val:2.0,count:1},{val:-5.0,count:1},{val:-9.0,count:1} ] } "
+ "}");
// test 0, min/max int/long
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{"
+ " u : 'unique(${Z_num_i})'"
+ ", u2 : 'unique(${Z_num_l})'"
+ ", min1 : 'min(${Z_num_i})', max1 : 'max(${Z_num_i})'"
+ ", min2 : 'min(${Z_num_l})', max2 : 'max(${Z_num_l})'"
+ ", f1:{${terms} type:field, field:${Z_num_i} }"
+ ", f2:{${terms} type:field, field:${Z_num_l} }"
+ "}"),
"facets=={count:6 "
+ ",u:3"
+ ",u2:3"
+ ",min1:"
+ Integer.MIN_VALUE
+ ",max1:"
+ Integer.MAX_VALUE
+ ",min2:"
+ Long.MIN_VALUE
+ ",max2:"
+ Long.MAX_VALUE
+ ",f1:{ buckets:[{val:"
+ Integer.MIN_VALUE
+ ",count:1},{val:0,count:1},{val:"
+ Integer.MAX_VALUE
+ ",count:1}]} "
+ ",f2:{ buckets:[{val:"
+ Long.MIN_VALUE
+ ",count:1},{val:0,count:1},{val:"
+ Long.MAX_VALUE
+ ",count:1}]} "
+ "}");
// multi-valued integer
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{ "
+ " c1:'unique(${num_is})', c2:'hll(${num_is})', c3:'missing(${num_is})'"
+ ", c4:'countvals(${num_is})', c5:'agg(countvals(${num_is}))'"
+ ",f1:{${terms} type:terms, field:${num_is} } "
+ "}"),
"facets=={ count:6 "
+ ", c1:5, c2:5, c3:1, c4:8, c5:8"
+ ", f1:{ buckets:[ {val:-1,count:2},{val:0,count:2},{val:3,count:2},{val:-5,count:1},{val:2,count:1} ] } "
+ "} ");
// multi-valued float
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{ "
+ " c1:'unique(${num_fs})', c2:'hll(${num_fs})', c3:'missing(${num_fs})', c4:'agg(missing(${num_fs}))', c5:'countvals(${num_fs})'"
+ ",f1:{${terms} type:terms, field:${num_fs} } "
+ "}"),
"facets=={ count:6 "
+ ", c1:5, c2:5, c3:1, c4:1, c5:8"
+ ", f1:{ buckets:[ {val:-1.5,count:2},{val:0.0,count:2},{val:3.0,count:2},{val:-5.0,count:1},{val:2.0,count:1} ] } "
+ "} ");
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{"
+
// "cat0:{type:terms, field:${cat_s}, sort:'count desc', limit:1, overrequest:0}" +
// // overrequest=0 test needs predictable layout
"cat1:{type:terms, field:${cat_s}, sort:'count desc', limit:1, overrequest:1}"
+ ",catDef:{type:terms, field:${cat_s}, sort:'count desc', limit:1, overrequest:-1}"
+ // -1 is default overrequest
",catBig:{type:terms, field:${cat_s}, sort:'count desc', offset:1, limit:2147483647, overrequest:2147483647}"
+ // make sure overflows don't mess us up
"}"),
"facets=={ count:6"
+
// ", cat0:{ buckets:[ {val:B,count:3} ] }"
", cat1:{ buckets:[ {val:B,count:3} ] }"
+ ", catDef:{ buckets:[ {val:B,count:3} ] }"
+ ", catBig:{ buckets:[ {val:A,count:2} ] }"
+ "}");
// test filter
client.testJQ(
params(
p,
"q",
"*:*",
"myfilt",
"${cat_s}:A",
"ff",
"-id:1",
"ff",
"-id:2",
"json.facet",
"{"
+ "t:{${terms} type:terms, field:${cat_s}, domain:{filter:[]} }"
+ // empty filter list
",t_filt:{${terms} type:terms, field:${cat_s}, domain:{filter:'${cat_s}:B'} }"
+ ",t_filt2 :{${terms} type:terms, field:${cat_s}, domain:{filter:'{!query v=$myfilt}'} }"
+ // test access to qparser and other query parameters
",t_filt2a:{${terms} type:terms, field:${cat_s}, domain:{filter:{param:myfilt} } }"
+ // test filter via "param" type
",t_filt3: {${terms} type:terms, field:${cat_s}, domain:{filter:['-id:1','-id:2']} }"
+ ",t_filt3a:{${terms} type:terms, field:${cat_s}, domain:{filter:{param:ff}} }"
+ // test multi-valued query parameter
",q:{type:query, q:'${cat_s}:B', domain:{filter:['-id:5']} }"
+ // also tests a top-level negative filter
",r:{type:range, field:${num_d}, start:-5, end:10, gap:5, domain:{filter:'-id:4'} }"
+ "}"),
"facets=={ count:6, "
+ "t :{ buckets:[ {val:B, count:3}, {val:A, count:2} ] }"
+ ",t_filt :{ buckets:[ {val:B, count:3}] } "
+ ",t_filt2 :{ buckets:[ {val:A, count:2}] } "
+ ",t_filt2a:{ buckets:[ {val:A, count:2}] } "
+ ",t_filt3 :{ buckets:[ {val:B, count:2}, {val:A, count:1}] } "
+ ",t_filt3a:{ buckets:[ {val:B, count:2}, {val:A, count:1}] } "
+ ",q:{count:2}"
+ ",r:{buckets:[ {val:-5.0,count:1}, {val:0.0,count:1}, {val:5.0,count:0} ] }"
+ "}");
// test filter using queries from json.queries
client.testJQ(
params(
p,
"q",
"*:*",
"json.queries",
"{catS:{'#cat_sA': '${cat_s}:A'}, ff:[{'#id_1':'-id:1'},{'#id_2':'-id:2'}]}",
"json.facet",
"{"
+ ",t_filt1:{${terms} type:terms, field:${cat_s}, domain:{filter:{param:catS} } }"
+ // test filter via "param" type from .queries
",t_filt2:{${terms} type:terms, field:${cat_s}, domain:{filter:{param:ff}} }"
+ // test multi-valued query parameter from .queries
"}"),
"facets=={ count:6, "
+ ",t_filt1:{ buckets:[ {val:A, count:2}] } "
+ ",t_filt2:{ buckets:[ {val:B, count:2}, {val:A, count:1}] } "
+ "}");
// test acc reuse (i.e. reset() method). This is normally used for stats that are not
// calculated in the first phase, currently non-sorting stats.
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{type:terms, field:'${cat_s}', facet:{h:'hll(${where_s})' , u:'unique(${where_s})', mind:'min(${num_d})', maxd:'max(${num_d})', mini:'min(${num_i})', maxi:'max(${num_i})'"
+ ", sumd:'sum(${num_d})', avgd:'avg(${num_d})', variance:'variance(${num_d})', stddev:'stddev(${num_d})', missing:'missing(${multi_ss})', vals:'countvals(${multi_ss})'} }}"),
"facets=={ 'count':6, "
+ "'f1':{ buckets:[{val:B, count:3, h:2, u:2, mind:-9.0, maxd:11.0, mini:-5, maxi:7, sumd:-3.0, avgd:-1.0, variance:112.0, stddev:10.583005244258363, missing:0, vals:5},"
+ " {val:A, count:2, h:2, u:2, mind:2.0, maxd:4.0, mini:2, maxi:3, sumd:6.0, avgd:3.0, variance:2.0, stddev:1.4142135623730951, missing:1, vals:1}] } } ");
// test min/max of string field
if (where_s.equals("where_s")
|| where_s.equals("where_sd")) { // supports only single valued currently...
client.testJQ(
params(
p,
"q",
"*:* -(+${cat_s}:A +${where_s}:NJ)", // make NY the only value in bucket A
"json.facet",
"{"
+ " f1:{type:terms, field:'${cat_s}', facet:{min:'min(${where_s})', max:'max(${where_s})'} }"
+ ", f2:{type:terms, field:'${cat_s}', facet:{min:'min(${where_s})', max:'max(${where_s})'} , sort:'min desc'}"
+ ", f3:{type:terms, field:'${cat_s}', facet:{min:'min(${where_s})', max:'max(${where_s})'} , sort:'min asc'}"
+ ", f4:{type:terms, field:'${cat_s}', facet:{min:'min(${super_s})', max:'max(${super_s})'} , sort:'max asc'}"
+ ", f5:{type:terms, field:'${cat_s}', facet:{min:'min(${super_s})', max:'max(${super_s})'} , sort:'max desc'}"
+ "}"),
"facets=={ count:5, "
+ " f1:{ buckets:[{val:B, count:3, min:NJ, max:NY}, {val:A, count:1, min:NY, max:NY}]}"
+ ",f2:{ buckets:[{val:A, count:1, min:NY, max:NY}, {val:B, count:3, min:NJ, max:NY}]}"
+ ",f3:{ buckets:[{val:B, count:3, min:NJ, max:NY}, {val:A, count:1, min:NY, max:NY}]}"
+ ",f4:{ buckets:[{val:B, count:3, min:batman, max:superman}, {val:A, count:1, min:zodiac, max:zodiac}]}"
+ ",f5:{ buckets:[{val:A, count:1, min:zodiac, max:zodiac}, {val:B, count:3, min:batman, max:superman}]}"
+ " } ");
}
////////////////////////////////////////////////////////////////
// test which phase stats are calculated in
////////////////////////////////////////////////////////////////
if (client.local()) {
long creates, resets;
// NOTE: these test the current implementation and may need to be adjusted to match future
// optimizations (such as calculating N buckets in parallel in the second phase)
creates = DebugAgg.Acc.creates.get();
resets = DebugAgg.Acc.resets.get();
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms_method} field:${super_s}, limit:1, facet:{x:'debug()'} }}}" // x should be deferred to 2nd phase
),
"facets=={ 'count':6, " + "f1:{ buckets:[{ val:batman, count:1, x:1}]} } ");
assertEquals(1, DebugAgg.Acc.creates.get() - creates);
assertTrue(DebugAgg.Acc.resets.get() - resets <= 1);
// probably "1", but may be special slot for something. As long as it's not cardinality of
// the field
assertTrue(DebugAgg.Acc.last.numSlots <= 2);
creates = DebugAgg.Acc.creates.get();
resets = DebugAgg.Acc.resets.get();
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms_method} field:${super_s}, limit:1, facet:{ x:'debug()'} , sort:'x asc' }}}" // sorting by x... must be done all at once in first phase
),
"facets=={ 'count':6, " + "f1:{ buckets:[{ val:batman, count:1, x:1}]}" + " } ");
assertEquals(1, DebugAgg.Acc.creates.get() - creates);
assertEquals(0, DebugAgg.Acc.resets.get() - resets);
// all slots should be done in a single shot. there may be more than 5 due to special slots or
// hashing.
assertTrue(DebugAgg.Acc.last.numSlots >= 5);
// When limit:-1, we should do most stats in first phase (SOLR-10634)
creates = DebugAgg.Acc.creates.get();
resets = DebugAgg.Acc.resets.get();
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms_method} field:${super_s}, limit:-1, facet:{x:'debug()'} }}}"),
"facets==");
assertEquals(1, DebugAgg.Acc.creates.get() - creates);
assertEquals(0, DebugAgg.Acc.resets.get() - resets);
// all slots should be done in a single shot. there may be more than 5 due to special slots or
// hashing.
assertTrue(DebugAgg.Acc.last.numSlots >= 5);
// Now for a numeric field
// When limit:-1, we should do most stats in first phase (SOLR-10634)
creates = DebugAgg.Acc.creates.get();
resets = DebugAgg.Acc.resets.get();
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms_method} field:${num_d}, limit:-1, facet:{x:'debug()'} }}}"),
"facets==");
assertEquals(1, DebugAgg.Acc.creates.get() - creates);
assertEquals(0, DebugAgg.Acc.resets.get() - resets);
// all slots should be done in a single shot. there may be more than 5 due to special slots or
// hashing.
assertTrue(DebugAgg.Acc.last.numSlots >= 5);
// But if we need to calculate domains anyway, it probably makes sense to calculate most stats
// in the 2nd phase (along with sub-facets)
creates = DebugAgg.Acc.creates.get();
resets = DebugAgg.Acc.resets.get();
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms_method} field:${super_s}, limit:-1, facet:{ x:'debug()' , y:{terms:${where_s}} } }}}"),
"facets==");
assertEquals(1, DebugAgg.Acc.creates.get() - creates);
assertTrue(DebugAgg.Acc.resets.get() - resets >= 4);
assertTrue(DebugAgg.Acc.last.numSlots <= 2); // probably 1, but could be higher
// Now with a numeric field
// But if we need to calculate domains anyway, it probably makes sense to calculate most stats
// in the 2nd phase (along with sub-facets)
creates = DebugAgg.Acc.creates.get();
resets = DebugAgg.Acc.resets.get();
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{terms:{${terms_method} field:${num_d}, limit:-1, facet:{ x:'debug()' , y:{terms:${where_s}} } }}}"),
"facets==");
assertEquals(1, DebugAgg.Acc.creates.get() - creates);
assertTrue(DebugAgg.Acc.resets.get() - resets >= 4);
assertTrue(DebugAgg.Acc.last.numSlots <= 2); // probably 1, but could be higher
}
//////////////////////////////////////////////////////////////// end phase testing
//
// Refinement should not be needed to get exact results here, so this tests that extra
// refinement requests are not sent out. This currently relies on counting the number of times
// debug() aggregation is parsed... which is somewhat fragile. Please replace this with
// something better in the future - perhaps debug level info about number of refinements or
// additional facet phases.
//
for (String facet_field :
new String[] {
cat_s, where_s, num_d, num_i, num_is, num_fs, super_s, date, val_b, multi_ss
}) {
ModifiableSolrParams test =
params(
p,
"q",
"id:(1 2)",
"facet_field",
facet_field,
"debug",
"true",
"json.facet",
"{ "
+ " f1:{type:terms, field:'${facet_field}', refine:${refine}, facet:{x:'debug()'} }"
+ ",f2:{type:terms, method:dvhash, field:'${facet_field}', refine:${refine}, facet:{x:'debug()'} }"
+ ",f3:{type:terms, field:'${facet_field}', refine:${refine}, facet:{x:'debug()', y:{type:terms,field:'${facet_field}',refine:${refine}}} }"
+ // facet within facet
" }");
long startParses = DebugAgg.parses.get();
client.testJQ(params(test, "refine", "false"), "facets==" + "");
long noRefineParses = DebugAgg.parses.get() - startParses;
startParses = DebugAgg.parses.get();
client.testJQ(params(test, "refine", "true"), "facets==" + "");
long refineParses = DebugAgg.parses.get() - startParses;
assertEquals(noRefineParses, refineParses);
}
}
public void testPrelimSortingSingleNode() throws Exception {
doTestPrelimSortingSingleNode(false, false);
}
public void testPrelimSortingSingleNodeExtraStat() throws Exception {
doTestPrelimSortingSingleNode(true, false);
}
public void testPrelimSortingSingleNodeExtraFacet() throws Exception {
doTestPrelimSortingSingleNode(false, true);
}
public void testPrelimSortingSingleNodeExtraStatAndFacet() throws Exception {
doTestPrelimSortingSingleNode(true, true);
}
/**
* @see #doTestPrelimSorting
*/
public void doTestPrelimSortingSingleNode(final boolean extraAgg, final boolean extraSubFacet)
throws Exception {
// we're not using Client.localClient because it doesn't provide a SolrClient to
// use in doTestPrelimSorting -- so instead we make a single node, and don't use any shards
// param...
final SolrInstances nodes = new SolrInstances(1, "solrconfig-tlog.xml", "schema_latest.xml");
try {
final Client client = nodes.getClient(random().nextInt());
client.queryDefaults().set("debugQuery", Boolean.toString(random().nextBoolean()));
doTestPrelimSorting(client, extraAgg, extraSubFacet);
} finally {
nodes.stop();
}
}
public void testPrelimSortingDistrib() throws Exception {
doTestPrelimSortingDistrib(false, false);
}
public void testPrelimSortingDistribExtraStat() throws Exception {
doTestPrelimSortingDistrib(true, false);
}
public void testPrelimSortingDistribExtraFacet() throws Exception {
doTestPrelimSortingDistrib(false, true);
}
public void testPrelimSortingDistribExtraStatAndFacet() throws Exception {
doTestPrelimSortingDistrib(true, true);
}
/**
* @see #doTestPrelimSorting
*/
public void doTestPrelimSortingDistrib(final boolean extraAgg, final boolean extraSubFacet)
throws Exception {
// we only use 2 shards, but we also want to check code paths if one (additional)
// shard is empty
final int totalShards = random().nextBoolean() ? 2 : 3;
final SolrInstances nodes =
new SolrInstances(totalShards, "solrconfig-tlog.xml", "schema_latest.xml");
try {
final Client client = nodes.getClient(random().nextInt());
client
.queryDefaults()
.set(
"shards", nodes.getShards(),
"debugQuery", Boolean.toString(random().nextBoolean()));
doTestPrelimSorting(client, extraAgg, extraSubFacet);
} finally {
nodes.stop();
}
}
/**
* Helper method that indexes a fixed set of docs to exactly <em>two</em> of the SolrClients
* involved in the current Client such that each shard is identical for the purposes of simplified
* doc/facet counting/assertions -- if there is only one SolrClient (Client.local) then it sends
* that single shard twice as many docs so the counts/assertions will be consistent.
*
* <p>Note: this test doesn't demonstrate practical uses of prelim_sort. The scenerios it tests
* are actualy fairly absurd, but help to ensure that edge cases are covered.
*
* @param client client to use -- may be local or multishard
* @param extraAgg if an extra aggregation function should be included, this hits slightly diff
* code paths
* @param extraSubFacet if an extra sub facet should be included, this hits slightly diff code
* paths
*/
public void doTestPrelimSorting(
final Client client, final boolean extraAgg, final boolean extraSubFacet) throws Exception {
client.deleteByQuery("*:*", null);
List<SolrClient> clients = client.getClientProvider().all();
// carefully craft two balanced shards (assuming we have at least two) and leave any other
// shards empty to help check the code paths of some shards returning no buckets.
//
// if we are in a single node situation, these clients will be the same, and we'll have the same
// total docs in our collection, but the numShardsWithData will be different
// (which will affect some assertions)
final SolrClient shardA = clients.get(0);
final SolrClient shardB = clients.get(clients.size() - 1);
final int numShardsWithData = (shardA == shardB) ? 1 : 2;
// for simplicity, each foo_s "term" exists on each shard in the same number of docs as it's
// numeric value (so count should be double the term) and bar_i is always 1 per doc (so
// sum(bar_i) should always be the same as count)
int id = 0;
for (int i = 1; i <= 20; i++) {
for (int j = 1; j <= i; j++) {
shardA.add(new SolrInputDocument("id", "" + (++id), "foo_s", "foo_" + i, "bar_i", "1"));
shardB.add(new SolrInputDocument("id", "" + (++id), "foo_s", "foo_" + i, "bar_i", "1"));
}
}
assertEquals(420, id); // sanity check
client.commit();
DebugAgg.Acc.collectDocs.set(0);
DebugAgg.Acc.collectDocSets.set(0);
// NOTE: sorting by index can cause some optimizations when using type=enum|stream
// that cause our stat to be collected differently, so we have to account for that when
// looking at DebugAdd collect stats if/when the test framework picks those
// ...BUT... this only affects cloud, for single node prelim_sort overrides streaming
final boolean indexSortDebugAggFudge =
(1 < numShardsWithData)
&& (FacetField.FacetMethod.DEFAULT_METHOD.equals(FacetField.FacetMethod.STREAM)
|| FacetField.FacetMethod.DEFAULT_METHOD.equals(FacetField.FacetMethod.ENUM));
final String common =
"refine:true, type:field, field:'foo_s', facet: { "
+ "x: 'debug(wrap,sum(bar_i))' "
+ (extraAgg ? ", y:'min(bar_i)'" : "")
+ (extraSubFacet ? ", z:{type:query, q:'bar_i:0'}" : "")
+ "}";
final String yz = (extraAgg ? "y:1, " : "") + (extraSubFacet ? "z:{count:0}, " : "");
// really basic: top 5 by (prelim_sort) count, (re)sorted by a stat
client.testJQ(
params(
"q",
"*:*",
"rows",
"0",
"json.facet",
"{ foo_a:{ "
+ common
+ ", limit:5, overrequest:0, "
+ " prelim_sort:'count desc', sort:'x asc' }"
+ " foo_b:{ "
+ common
+ ", limit:5, overrequest:0, "
+ " prelim_sort:'count asc', sort:'x desc' } }"),
"facets=={ 'count':420, "
+ " 'foo_a':{ 'buckets':["
+ " { val:foo_16, count:32, "
+ yz
+ "x:32.0},"
+ " { val:foo_17, count:34, "
+ yz
+ "x:34.0},"
+ " { val:foo_18, count:36, "
+ yz
+ "x:36.0},"
+ " { val:foo_19, count:38, "
+ yz
+ "x:38.0},"
+ " { val:foo_20, count:40, "
+ yz
+ "x:40.0},"
+ "] },"
+ " 'foo_b':{ 'buckets':["
+ " { val:foo_5, count:10, "
+ yz
+ "x:10.0},"
+ " { val:foo_4, count:8, "
+ yz
+ "x:8.0},"
+ " { val:foo_3, count:6, "
+ yz
+ "x:6.0},"
+ " { val:foo_2, count:4, "
+ yz
+ "x:4.0},"
+ " { val:foo_1, count:2, "
+ yz
+ "x:2.0},"
+ "] },"
+ "}");
// (re)sorting should prevent 'sum(bar_i)' from being computed for every doc
// only the choosen buckets should be collected (as a set) once per node...
assertEqualsAndReset(0, DebugAgg.Acc.collectDocs);
// 2 facets, 5 bucket, on each shard
assertEqualsAndReset(numShardsWithData * 2 * 5, DebugAgg.Acc.collectDocSets);
{ // same really basic top 5 by (prelim_sort) count, (re)sorted by a stat -- w/allBuckets:true
// check code paths with and w/o allBuckets
// NOTE: allBuckets includes stats, but not other sub-facets...
final String aout = "allBuckets:{ count:420, " + (extraAgg ? "y:1, " : "") + "x:420.0 }";
client.testJQ(
params(
"q",
"*:*",
"rows",
"0",
"json.facet",
"{ foo_a:{ "
+ common
+ ", allBuckets:true, limit:5, overrequest:0, "
+ " prelim_sort:'count desc', sort:'x asc' }"
+ " foo_b:{ "
+ common
+ ", allBuckets:true, limit:5, overrequest:0, "
+ " prelim_sort:'count asc', sort:'x desc' } }"),
"facets=={ 'count':420, "
+ " 'foo_a':{ "
+ aout
+ " 'buckets':["
+ " { val:foo_16, count:32, "
+ yz
+ "x:32.0},"
+ " { val:foo_17, count:34, "
+ yz
+ "x:34.0},"
+ " { val:foo_18, count:36, "
+ yz
+ "x:36.0},"
+ " { val:foo_19, count:38, "
+ yz
+ "x:38.0},"
+ " { val:foo_20, count:40, "
+ yz
+ "x:40.0},"
+ "] },"
+ " 'foo_b':{ "
+ aout
+ " 'buckets':["
+ " { val:foo_5, count:10, "
+ yz
+ "x:10.0},"
+ " { val:foo_4, count:8, "
+ yz
+ "x:8.0},"
+ " { val:foo_3, count:6, "
+ yz
+ "x:6.0},"
+ " { val:foo_2, count:4, "
+ yz
+ "x:4.0},"
+ " { val:foo_1, count:2, "
+ yz
+ "x:2.0},"
+ "] },"
+ "}");
// because of allBuckets, we collect every doc on everyshard (x2 facets) in a single "all"
// slot...
assertEqualsAndReset(2 * 420, DebugAgg.Acc.collectDocs);
// ... in addition to collecting each of the choosen buckets (as sets) once per node...
// 2 facets, 5 bucket, on each shard
assertEqualsAndReset(numShardsWithData * 2 * 5, DebugAgg.Acc.collectDocSets);
}
// pagination (with offset) should happen against the re-sorted list (up to the effective limit)
client.testJQ(
params(
"q",
"*:*",
"rows",
"0",
"json.facet",
"{ foo_a:{ "
+ common
+ ", offset:2, limit:3, overrequest:0, "
+ " prelim_sort:'count desc', sort:'x asc' }"
+ " foo_b:{ "
+ common
+ ", offset:2, limit:3, overrequest:0, "
+ " prelim_sort:'count asc', sort:'x desc' } }"),
"facets=={ 'count':420, "
+ " 'foo_a':{ 'buckets':["
+ " { val:foo_18, count:36, "
+ yz
+ "x:36.0},"
+ " { val:foo_19, count:38, "
+ yz
+ "x:38.0},"
+ " { val:foo_20, count:40, "
+ yz
+ "x:40.0},"
+ "] },"
+ " 'foo_b':{ 'buckets':["
+ " { val:foo_3, count:6, "
+ yz
+ "x:6.0},"
+ " { val:foo_2, count:4, "
+ yz
+ "x:4.0},"
+ " { val:foo_1, count:2, "
+ yz
+ "x:2.0},"
+ "] },"
+ "}");
assertEqualsAndReset(0, DebugAgg.Acc.collectDocs);
// 2 facets, 5 buckets (including offset), on each shard
assertEqualsAndReset(numShardsWithData * 2 * 5, DebugAgg.Acc.collectDocSets);
// when overrequesting is used, the full list of candidate buckets should be considered
client.testJQ(
params(
"q",
"*:*",
"rows",
"0",
"json.facet",
"{ foo_a:{ "
+ common
+ ", limit:5, overrequest:5, "
+ " prelim_sort:'count desc', sort:'x asc' }"
+ " foo_b:{ "
+ common
+ ", limit:5, overrequest:5, "
+ " prelim_sort:'count asc', sort:'x desc' } }"),
"facets=={ 'count':420, "
+ " 'foo_a':{ 'buckets':["
+ " { val:foo_11, count:22, "
+ yz
+ "x:22.0},"
+ " { val:foo_12, count:24, "
+ yz
+ "x:24.0},"
+ " { val:foo_13, count:26, "
+ yz
+ "x:26.0},"
+ " { val:foo_14, count:28, "
+ yz
+ "x:28.0},"
+ " { val:foo_15, count:30, "
+ yz
+ "x:30.0},"
+ "] },"
+ " 'foo_b':{ 'buckets':["
+ " { val:foo_10, count:20, "
+ yz
+ "x:20.0},"
+ " { val:foo_9, count:18, "
+ yz
+ "x:18.0},"
+ " { val:foo_8, count:16, "
+ yz
+ "x:16.0},"
+ " { val:foo_7, count:14, "
+ yz
+ "x:14.0},"
+ " { val:foo_6, count:12, "
+ yz
+ "x:12.0},"
+ "] },"
+ "}");
assertEqualsAndReset(0, DebugAgg.Acc.collectDocs);
// 2 facets, 10 buckets (including overrequest), on each shard
assertEqualsAndReset(numShardsWithData * 2 * 10, DebugAgg.Acc.collectDocSets);
{ // for an (effectively) unlimited facet, then from the black box perspective of the client,
// preliminary sorting should be completely ignored...
final StringBuilder expected =
new StringBuilder("facets=={ 'count':420, 'foo_a':{ 'buckets':[\n");
for (int i = 20; 0 < i; i--) {
final int x = i * 2;
expected.append("{ val:foo_" + i + ", count:" + x + ", " + yz + "x:" + x + ".0},\n");
}
expected.append("] } }");
for (int limit : Arrays.asList(-1, 100000)) {
for (String sortOpts :
Arrays.asList(
"sort:'x desc'",
"prelim_sort:'count asc', sort:'x desc'",
"prelim_sort:'index asc', sort:'x desc'")) {
final String snippet = "limit: " + limit + ", " + sortOpts;
client.testJQ(
params(
"q",
"*:*",
"rows",
"0",
"json.facet",
"{ foo_a:{ " + common + ", " + snippet + "}}"),
expected.toString());
// the only difference from a white box perspective, is when/if we are
// optimized to use the sort SlotAcc during collection instead of the prelim_sort
// SlotAcc...
// (ie: sub facet preventing single pass (re)sort in single node mode)
if (((0 < limit || extraSubFacet) && snippet.contains("prelim_sort"))
&& !(indexSortDebugAggFudge && snippet.contains("index asc"))) {
// by-pass single pass collection, do everything as sets...
assertEqualsAndReset(snippet, numShardsWithData * 20, DebugAgg.Acc.collectDocSets);
assertEqualsAndReset(snippet, 0, DebugAgg.Acc.collectDocs);
} else { // simple sort on x, or optimized single pass (re)sort, or indexSortDebugAggFudge
// no sets should have been (post) collected for our stat
assertEqualsAndReset(snippet, 0, DebugAgg.Acc.collectDocSets);
// every doc should be collected...
assertEqualsAndReset(snippet, 420, DebugAgg.Acc.collectDocs);
}
}
}
}
// test all permutations of (prelim_sort | sort) on (index | count | stat) since there are
// custom sort codepaths for index & count that work differnetly then general stats
//
// NOTE: there's very little value in re-sort by count/index after prelim_sort on something more
// complex, typically better to just ignore the prelim_sort, but we're testing it for
// completeness (and because you *might* want to prelim_sort by some function, for the purpose
// of "sampling" the top results and then (re)sorting by count/index)
for (String numSort : Arrays.asList("count", "x")) { // equivalent ordering
client.testJQ(
params(
"q",
"*:*",
"rows",
"0",
"json.facet",
"{ foo_a:{ "
+ common
+ ", limit:10, overrequest:0, "
+ " prelim_sort:'"
+ numSort
+ " asc', sort:'index desc' }"
+ " foo_b:{ "
+ common
+ ", limit:10, overrequest:0, "
+ " prelim_sort:'index asc', sort:'"
+ numSort
+ " desc' } }"),
"facets=={ 'count':420, "
+ " 'foo_a':{ 'buckets':["
+ " { val:foo_9, count:18, "
+ yz
+ "x:18.0},"
+ " { val:foo_8, count:16, "
+ yz
+ "x:16.0},"
+ " { val:foo_7, count:14, "
+ yz
+ "x:14.0},"
+ " { val:foo_6, count:12, "
+ yz
+ "x:12.0},"
+ " { val:foo_5, count:10, "
+ yz
+ "x:10.0},"
+ " { val:foo_4, count:8, "
+ yz
+ "x:8.0},"
+ " { val:foo_3, count:6, "
+ yz
+ "x:6.0},"
+ " { val:foo_2, count:4, "
+ yz
+ "x:4.0},"
+ " { val:foo_10, count:20, "
+ yz
+ "x:20.0},"
+ " { val:foo_1, count:2, "
+ yz
+ "x:2.0},"
+ "] },"
+ " 'foo_b':{ 'buckets':["
+ " { val:foo_18, count:36, "
+ yz
+ "x:36.0},"
+ " { val:foo_17, count:34, "
+ yz
+ "x:34.0},"
+ " { val:foo_16, count:32, "
+ yz
+ "x:32.0},"
+ " { val:foo_15, count:30, "
+ yz
+ "x:30.0},"
+ " { val:foo_14, count:28, "
+ yz
+ "x:28.0},"
+ " { val:foo_13, count:26, "
+ yz
+ "x:26.0},"
+ " { val:foo_12, count:24, "
+ yz
+ "x:24.0},"
+ " { val:foo_11, count:22, "
+ yz
+ "x:22.0},"
+ " { val:foo_10, count:20, "
+ yz
+ "x:20.0},"
+ " { val:foo_1, count:2, "
+ yz
+ "x:2.0},"
+ "] },"
+ "}");
// since these behave differently, defer DebugAgg counter checks until all are done...
}
// These 3 permutations defer the computation of x as docsets,
// so it's 3 x (10 buckets on each shard) (but 0 direct docs)
// prelim_sort:count, sort:index
// prelim_sort:index, sort:x
// prelim_sort:index, sort:count
// ...except when streaming, prelim_sort:index does no docsets.
assertEqualsAndReset(
(indexSortDebugAggFudge ? 1 : 3) * numShardsWithData * 10, DebugAgg.Acc.collectDocSets);
// This is the only situation that should (always) result in every doc being collected (but 0
// docsets)...
// prelim_sort:x, sort:index
// ...but the (2) prelim_sort:index streaming situations above will also cause all the docs in
// the first
// 10+1 buckets to be collected (enum checks limit+1) to know if there are "more"...
assertEqualsAndReset(
420
+ (indexSortDebugAggFudge
? 2 * numShardsWithData * (1 + 10 + 11 + 12 + 13 + 14 + 15 + 16 + 17 + 18 + 19)
: 0),
DebugAgg.Acc.collectDocs);
// sanity check of prelim_sorting in a sub facet
client.testJQ(
params(
"q",
"*:*",
"rows",
"0",
"json.facet",
"{ bar:{ type:query, query:'foo_s:[foo_10 TO foo_19]', facet: {"
+ " foo:{ "
+ common
+ ", limit:5, overrequest:0, "
+ " prelim_sort:'count desc', sort:'x asc' } } } }"),
"facets=={ 'count':420, "
+ " 'bar':{ 'count':290, "
+ " 'foo':{ 'buckets':["
+ " { val:foo_15, count:30, "
+ yz
+ "x:30.0},"
+ " { val:foo_16, count:32, "
+ yz
+ "x:32.0},"
+ " { val:foo_17, count:34, "
+ yz
+ "x:34.0},"
+ " { val:foo_18, count:36, "
+ yz
+ "x:36.0},"
+ " { val:foo_19, count:38, "
+ yz
+ "x:38.0},"
+ " ] },"
+ " },"
+ "}");
// the prelim_sort should prevent 'sum(bar_i)' from being computed for every doc
// only the chosen buckets should be collected (as a set) once per node...
assertEqualsAndReset(0, DebugAgg.Acc.collectDocs);
// 5 bucket, on each shard
assertEqualsAndReset(numShardsWithData * 5, DebugAgg.Acc.collectDocSets);
{ // check how deferred stats are handled
// here we'll prelim_sort & sort on things that are both "not x" and using the debug()
// counters (wrapping x) to assert that 'x' is correctly deferred and only collected for the
// final top buckets
final List<String> sorts = new ArrayList<String>(Arrays.asList("index asc", "count asc"));
if (extraAgg) {
sorts.add("y asc"); // same for every bucket, but index order tiebreaker should kick in
}
for (String s : sorts) {
client.testJQ(
params(
"q",
"*:*",
"rows",
"0",
"json.facet",
"{ foo:{ "
+ common
+ ", limit:5, overrequest:0, "
+ " prelim_sort:'count desc', sort:'"
+ s
+ "' } }"),
"facets=={ 'count':420, "
+ " 'foo':{ 'buckets':["
+ " { val:foo_16, count:32, "
+ yz
+ "x:32.0},"
+ " { val:foo_17, count:34, "
+ yz
+ "x:34.0},"
+ " { val:foo_18, count:36, "
+ yz
+ "x:36.0},"
+ " { val:foo_19, count:38, "
+ yz
+ "x:38.0},"
+ " { val:foo_20, count:40, "
+ yz
+ "x:40.0},"
+ "] } }");
// Neither prelim_sort nor sort should need 'sum(bar_i)' to be computed for every doc
// only the chosen buckets should be collected (as a set) once per node...
assertEqualsAndReset(0, DebugAgg.Acc.collectDocs);
// 5 bucket, on each shard
assertEqualsAndReset(numShardsWithData * 5, DebugAgg.Acc.collectDocSets);
}
}
}
@Test
public void testOverrequest() throws Exception {
initServers();
Client client = servers.getClient(random().nextInt());
client
.queryDefaults()
.set("shards", servers.getShards())
.set("debugQuery", Boolean.toString(random().nextBoolean()));
List<SolrClient> clients = client.getClientProvider().all();
assertTrue(clients.size() >= 3);
client.deleteByQuery("*:*", null);
ModifiableSolrParams p = params("cat_s", "cat_s");
String cat_s = p.get("cat_s");
clients.get(0).add(sdoc("id", "1", cat_s, "A")); // A will win tiebreak
clients.get(0).add(sdoc("id", "2", cat_s, "B"));
clients.get(1).add(sdoc("id", "3", cat_s, "B"));
clients.get(1).add(sdoc("id", "4", cat_s, "A")); // A will win tiebreak
clients.get(2).add(sdoc("id", "5", cat_s, "B"));
clients.get(2).add(sdoc("id", "6", cat_s, "B"));
client.commit();
// Shard responses should be A=1, A=1, B=2, merged should be "A=2, B=2" hence A wins tiebreak
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{"
+ "cat0:{type:terms, field:${cat_s}, sort:'count desc', limit:1, overrequest:0}"
+ ",cat1:{type:terms, field:${cat_s}, sort:'count desc', limit:1, overrequest:1}"
+ ",catDef:{type:terms, field:${cat_s}, sort:'count desc', limit:1, overrequest:-1}"
+ // -1 is default overrequest
",catBig:{type:terms, field:${cat_s}, sort:'count desc', offset:1, limit:2147483647, overrequest:2147483647}"
+ // make sure overflows don't mess us up
"}"),
"facets=={ count:6"
+ ", cat0:{ buckets:[ {val:A,count:2} ] }"
+ // with no overrequest, we incorrectly conclude that A is the top bucket
", cat1:{ buckets:[ {val:B,count:4} ] }"
+ ", catDef:{ buckets:[ {val:B,count:4} ] }"
+ ", catBig:{ buckets:[ {val:A,count:2} ] }"
+ "}");
}
@Test
public void testBigger() throws Exception {
ModifiableSolrParams p = params("rows", "0", "cat_s", "cat_ss", "where_s", "where_ss");
// doBigger(Client.localClient, p);
initServers();
Client client = servers.getClient(random().nextInt());
client.queryDefaults().set("shards", servers.getShards());
doBigger(client, p);
}
private String getId(int id) {
return String.format(Locale.US, "%05d", id);
}
public void doBigger(Client client, ModifiableSolrParams p) throws Exception {
MacroExpander m = new MacroExpander(p.getMap());
String cat_s = m.expand("${cat_s}");
String where_s = m.expand("${where_s}");
client.deleteByQuery("*:*", null);
Random r = new Random(0); // make deterministic
int numCat = 1;
int numWhere = 2000000000;
int commitPercent = 10;
int ndocs = 1000;
Map<Integer, Map<Integer, List<Integer>>> model = new HashMap<>(); // cat->where->list<ids>
for (int i = 0; i < ndocs; i++) {
Integer cat = r.nextInt(numCat);
Integer where = r.nextInt(numWhere);
client.add(sdoc("id", getId(i), cat_s, cat, where_s, where), null);
Map<Integer, List<Integer>> sub = model.get(cat);
if (sub == null) {
sub = new HashMap<>();
model.put(cat, sub);
}
List<Integer> ids = sub.get(where);
if (ids == null) {
ids = new ArrayList<>();
sub.put(where, ids);
}
ids.add(i);
if (r.nextInt(100) < commitPercent) {
client.commit();
}
}
client.commit();
int sz = model.get(0).size();
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{type:terms, field:${cat_s}, limit:2, facet:{x:'unique($where_s)'} }}"),
"facets=={ 'count':"
+ ndocs
+ ","
+ "'f1':{ 'buckets':[{ 'val':'0', 'count':"
+ ndocs
+ ", x:"
+ sz
+ " }]} } ");
if (client.local()) {
// distrib estimation prob won't match
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{f1:{type:terms, field:${cat_s}, limit:2, facet:{x:'hll($where_s)'} }}"),
"facets=={ 'count':"
+ ndocs
+ ","
+ "'f1':{ 'buckets':[{ 'val':'0', 'count':"
+ ndocs
+ ", x:"
+ sz
+ " }]} } ");
}
client.testJQ(
params(p, "q", "*:*", "json.facet", "{f1:{type:terms, field:id, limit:1, offset:990}}"),
"facets=={ 'count':" + ndocs + "," + "'f1':{buckets:[{val:'00990',count:1}]}} ");
for (int i = 0; i < 20; i++) {
int off = random().nextInt(ndocs);
client.testJQ(
params(
p,
"q",
"*:*",
"off",
Integer.toString(off),
"json.facet",
"{f1:{type:terms, field:id, limit:1, offset:${off}}}"),
"facets=={ 'count':"
+ ndocs
+ ","
+ "'f1':{buckets:[{val:'"
+ getId(off)
+ "',count:1}]}} ");
}
}
public void testTolerant() throws Exception {
initServers();
Client client = servers.getClient(random().nextInt());
client
.queryDefaults()
.set("shards", servers.getShards() + "," + DEAD_HOST_1 + "/ignore_exception");
indexSimple(client);
try {
client.testJQ(
params(
"ignore_exception",
"true",
"shards.tolerant",
"false",
"q",
"*:*",
"json.facet",
"{f:{type:terms, field:cat_s}}"),
"facets=={ count:6," + "f:{ buckets:[{val:B,count:3},{val:A,count:2}] }" + "}");
fail("we should have failed");
} catch (Exception e) {
// ok
}
client.testJQ(
params(
"ignore_exception",
"true",
"shards.tolerant",
"true",
"q",
"*:*",
"json.facet",
"{f:{type:terms, field:cat_s}}"),
"facets=={ count:6," + "f:{ buckets:[{val:B,count:3},{val:A,count:2}] }" + "}");
}
@Test
public void testBlockJoin() throws Exception {
doBlockJoin(Client.localClient());
}
public void doBlockJoin(Client client) throws Exception {
ModifiableSolrParams p = params("rows", "0");
client.deleteByQuery("*:*", null);
SolrInputDocument parent;
parent = sdoc("id", "1", "type_s", "book", "book_s", "A", "v_t", "q");
client.add(parent, null);
parent = sdoc("id", "2", "type_s", "book", "book_s", "B", "v_t", "q w");
parent.addChildDocument(sdoc("id", "2.1", "type_s", "page", "page_s", "a", "v_t", "x y z"));
parent.addChildDocument(sdoc("id", "2.2", "type_s", "page", "page_s", "b", "v_t", "x y "));
parent.addChildDocument(sdoc("id", "2.3", "type_s", "page", "page_s", "c", "v_t", " y z"));
client.add(parent, null);
parent = sdoc("id", "3", "type_s", "book", "book_s", "C", "v_t", "q w e");
parent.addChildDocument(sdoc("id", "3.1", "type_s", "page", "page_s", "d", "v_t", "x "));
parent.addChildDocument(sdoc("id", "3.2", "type_s", "page", "page_s", "e", "v_t", " y "));
parent.addChildDocument(sdoc("id", "3.3", "type_s", "page", "page_s", "f", "v_t", " z"));
client.add(parent, null);
parent = sdoc("id", "4", "type_s", "book", "book_s", "D", "v_t", "e");
client.add(parent, null);
client.commit();
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{ "
+ "pages:{ type:query, domain:{blockChildren:'type_s:book'} , facet:{ x:{field:v_t} } }"
+ ",pages2:{type:terms, field:v_t, domain:{blockChildren:'type_s:book'} }"
+ ",books:{ type:query, domain:{blockParent:'type_s:book'} , facet:{ x:{field:v_t} } }"
+ ",books2:{type:terms, field:v_t, domain:{blockParent:'type_s:book'} }"
+ ",pageof3:{ type:query, q:'id:3', facet : { x : { type:terms, field:page_s, domain:{blockChildren:'type_s:book'}}} }"
+ ",bookof22:{ type:query, q:'id:2.2', facet : { x : { type:terms, field:book_s, domain:{blockParent:'type_s:book'}}} }"
+ ",missing_blockParent:{ type:query, domain:{blockParent:'type_s:does_not_exist'} }"
+ ",missing_blockChildren:{ type:query, domain:{blockChildren:'type_s:does_not_exist'} }"
+ "}"),
"facets=={ count:10"
+ ", pages:{count:6 , x:{buckets:[ {val:y,count:4},{val:x,count:3},{val:z,count:3} ]} }"
+ ", pages2:{ buckets:[ {val:y,count:4},{val:x,count:3},{val:z,count:3} ] }"
+ ", books:{count:4 , x:{buckets:[ {val:q,count:3},{val:e,count:2},{val:w,count:2} ]} }"
+ ", books2:{ buckets:[ {val:q,count:3},{val:e,count:2},{val:w,count:2} ] }"
+ ", pageof3:{count:1 , x:{buckets:[ {val:d,count:1},{val:e,count:1},{val:f,count:1} ]} }"
+ ", bookof22:{count:1 , x:{buckets:[ {val:B,count:1} ]} }"
+ ", missing_blockParent:{count:0}"
+ ", missing_blockChildren:{count:0}"
+ "}");
// no matches in base query
client.testJQ(
params(
"q",
"no_match_s:NO_MATCHES",
"json.facet",
"{ processEmpty:true,"
+ "pages:{ type:query, domain:{blockChildren:'type_s:book'} }"
+ ",books:{ type:query, domain:{blockParent:'type_s:book'} }"
+ "}"),
"facets=={ count:0" + ", pages:{count:0}" + ", books:{count:0}" + "}");
// test facet on children nested under terms facet on parents
client.testJQ(
params(
"q",
"*:*",
"json.facet",
"{"
+ "books:{ type:terms, field:book_s, facet:{ pages:{type:terms, field:v_t, domain:{blockChildren:'type_s:book'}} } }"
+ "}"),
"facets=={ count:10"
+ ", books:{buckets:[{val:A,count:1,pages:{buckets:[]}}"
+ " ,{val:B,count:1,pages:{buckets:[{val:y,count:3},{val:x,count:2},{val:z,count:2}]}}"
+ " ,{val:C,count:1,pages:{buckets:[{val:x,count:1},{val:y,count:1},{val:z,count:1}]}}"
+ " ,{val:D,count:1,pages:{buckets:[]}}"
+ "] }"
+ "}");
// test filter after block join
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{ "
+ "pages1:{type:terms, field:v_t, domain:{blockChildren:'type_s:book', filter:'*:*'} }"
+ ",pages2:{type:terms, field:v_t, domain:{blockChildren:'type_s:book', filter:'-id:3.1'} }"
+ ",books:{type:terms, field:v_t, domain:{blockParent:'type_s:book', filter:'*:*'} }"
+ ",books2:{type:terms, field:v_t, domain:{blockParent:'type_s:book', filter:'id:1'} }"
+ "}"),
"facets=={ count:10"
+ ", pages1:{ buckets:[ {val:y,count:4},{val:x,count:3},{val:z,count:3} ] }"
+ ", pages2:{ buckets:[ {val:y,count:4},{val:z,count:3},{val:x,count:2} ] }"
+ ", books:{ buckets:[ {val:q,count:3},{val:e,count:2},{val:w,count:2} ] }"
+ ", books2:{ buckets:[ {val:q,count:1} ] }"
+ "}");
// test other various ways to get filters
client.testJQ(
params(
p,
"q",
"*:*",
"f1",
"-id:3.1",
"f2",
"id:1",
"json.facet",
"{ "
+ "pages1:{type:terms, field:v_t, domain:{blockChildren:'type_s:book', filter:[]} }"
+ ",pages2:{type:terms, field:v_t, domain:{blockChildren:'type_s:book', filter:{param:f1} } }"
+ ",books:{type:terms, field:v_t, domain:{blockParent:'type_s:book', filter:[{param:q},{param:missing_param}]} }"
+ ",books2:{type:terms, field:v_t, domain:{blockParent:'type_s:book', filter:[{param:f2}] } }"
+ "}"),
"facets=={ count:10"
+ ", pages1:{ buckets:[ {val:y,count:4},{val:x,count:3},{val:z,count:3} ] }"
+ ", pages2:{ buckets:[ {val:y,count:4},{val:z,count:3},{val:x,count:2} ] }"
+ ", books:{ buckets:[ {val:q,count:3},{val:e,count:2},{val:w,count:2} ] }"
+ ", books2:{ buckets:[ {val:q,count:1} ] }"
+ "}");
}
/** An explicit test for unique*(_root_) across all methods */
public void testUniquesForMethod() throws Exception {
final Client client = Client.localClient();
final SolrParams p = params("rows", "0");
client.deleteByQuery("*:*", null);
SolrInputDocument parent;
parent = sdoc("id", "1", "type_s", "book", "book_s", "A", "v_t", "q");
client.add(parent, null);
parent = sdoc("id", "2", "type_s", "book", "book_s", "B", "v_t", "q w");
parent.addChildDocument(sdoc("id", "2.1", "type_s", "page", "page_s", "a", "v_t", "x y z"));
parent.addChildDocument(sdoc("id", "2.2", "type_s", "page", "page_s", "a", "v_t", "x1 z"));
parent.addChildDocument(sdoc("id", "2.3", "type_s", "page", "page_s", "a", "v_t", "x2 z"));
parent.addChildDocument(sdoc("id", "2.4", "type_s", "page", "page_s", "b", "v_t", "x y "));
parent.addChildDocument(sdoc("id", "2.5", "type_s", "page", "page_s", "c", "v_t", " y z"));
parent.addChildDocument(sdoc("id", "2.6", "type_s", "page", "page_s", "c", "v_t", " z"));
client.add(parent, null);
parent = sdoc("id", "3", "type_s", "book", "book_s", "C", "v_t", "q w e");
parent.addChildDocument(sdoc("id", "3.1", "type_s", "page", "page_s", "b", "v_t", "x y "));
parent.addChildDocument(sdoc("id", "3.2", "type_s", "page", "page_s", "d", "v_t", "x "));
parent.addChildDocument(sdoc("id", "3.3", "type_s", "page", "page_s", "e", "v_t", " y "));
parent.addChildDocument(sdoc("id", "3.4", "type_s", "page", "page_s", "f", "v_t", " z"));
client.add(parent, null);
parent = sdoc("id", "4", "type_s", "book", "book_s", "D", "v_t", "e");
client.add(parent, null);
client.commit();
client.testJQ(
params(
p,
"q",
"type_s:page",
"json.facet",
"{"
+ " types: {"
+ " type:terms,"
+ " field:type_s,"
+ " limit:-1,"
+ " facet: {"
+ " in_books: \"unique(_root_)\","
+ " via_field:\"uniqueBlock(_root_)\","
+ " via_query:\"uniqueBlock({!v=type_s:book})\" }"
+ " },"
+ " pages: {"
+ " type:terms,"
+ " field:page_s,"
+ " limit:-1,"
+ " facet: {"
+ " in_books: \"unique(_root_)\","
+ " via_field:\"uniqueBlock(_root_)\","
+ " via_query:\"uniqueBlock({!v=type_s:book})\" }"
+ " }"
+ "}"),
"response=={numFound:10,start:0,numFoundExact:true,docs:[]}",
"facets=={ count:10,"
+ "types:{"
+ " buckets:[ {val:page, count:10, in_books:2, via_field:2, via_query:2 } ]}"
+ "pages:{"
+ " buckets:[ "
+ " {val:a, count:3, in_books:1, via_field:1, via_query:1},"
+ " {val:b, count:2, in_books:2, via_field:2, via_query:2},"
+ " {val:c, count:2, in_books:1, via_field:1, via_query:1},"
+ " {val:d, count:1, in_books:1, via_field:1, via_query:1},"
+ " {val:e, count:1, in_books:1, via_field:1, via_query:1},"
+ " {val:f, count:1, in_books:1, via_field:1, via_query:1}"
+ " ]}"
+ "}");
}
/**
* Similar to {@link #testBlockJoin} but uses query time joining.
*
* <p>(asserts are slightly diff because if a query matches multiple types of documents, blockJoin
* domain switches to parent/child domains preserve any existing parent/children from the original
* domain - eg: when q=*:*)
*/
public void testQueryJoinBooksAndPages() throws Exception {
final Client client = Client.localClient();
final SolrParams p = params("rows", "0");
client.deleteByQuery("*:*", null);
// build up a list of the docs we want to test with
List<SolrInputDocument> docsToAdd = new ArrayList<>(10);
docsToAdd.add(sdoc("id", "1", "type_s", "book", "book_s", "A", "v_t", "q"));
docsToAdd.add(sdoc("id", "2", "type_s", "book", "book_s", "B", "v_t", "q w"));
docsToAdd.add(
sdoc("book_id_s", "2", "id", "2.1", "type_s", "page", "page_s", "a", "v_t", "x y z"));
docsToAdd.add(
sdoc("book_id_s", "2", "id", "2.2", "type_s", "page", "page_s", "b", "v_t", "x y "));
docsToAdd.add(
sdoc("book_id_s", "2", "id", "2.3", "type_s", "page", "page_s", "c", "v_t", " y z"));
docsToAdd.add(sdoc("id", "3", "type_s", "book", "book_s", "C", "v_t", "q w e"));
docsToAdd.add(
sdoc("book_id_s", "3", "id", "3.1", "type_s", "page", "page_s", "d", "v_t", "x "));
docsToAdd.add(
sdoc("book_id_s", "3", "id", "3.2", "type_s", "page", "page_s", "e", "v_t", " y "));
docsToAdd.add(
sdoc("book_id_s", "3", "id", "3.3", "type_s", "page", "page_s", "f", "v_t", " z"));
docsToAdd.add(sdoc("id", "4", "type_s", "book", "book_s", "D", "v_t", "e"));
// shuffle the docs since order shouldn't matter
Collections.shuffle(docsToAdd, random());
for (SolrInputDocument doc : docsToAdd) {
client.add(doc, null);
}
client.commit();
// the domains we'll be testing, initially setup for block join
final String toChildren = "join: { from:'id', to:'book_id_s' }";
final String toParents = "join: { from:'book_id_s', to:'id' }";
final String toBogusChildren = "join: { from:'id', to:'does_not_exist_s' }";
final String toBogusParents = "join: { from:'book_id_s', to:'does_not_exist_s' }";
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{ "
+ "pages:{ type:query, domain:{"
+ toChildren
+ "} , facet:{ x:{field:v_t} } }"
+ ",pages2:{type:terms, field:v_t, domain:{"
+ toChildren
+ "} }"
+ ",books:{ type:query, domain:{"
+ toParents
+ "} , facet:{ x:{field:v_t} } }"
+ ",books2:{type:terms, field:v_t, domain:{"
+ toParents
+ "} }"
+ ",pageof3:{ type:query, q:'id:3', facet : { x : { type:terms, field:page_s, domain:{"
+ toChildren
+ "}}} }"
+ ",bookof22:{ type:query, q:'id:2.2', facet : { x : { type:terms, field:book_s, domain:{"
+ toParents
+ "}}} }"
+ ",missing_Parents:{ type:query, domain:{"
+ toBogusParents
+ "} }"
+ ",missing_Children:{ type:query, domain:{"
+ toBogusChildren
+ "} }"
+ "}"),
"facets=={ count:10"
+ ", pages:{count:6 , x:{buckets:[ {val:y,count:4},{val:x,count:3},{val:z,count:3} ]} }"
+ ", pages2:{ buckets:[ {val:y,count:4},{val:x,count:3},{val:z,count:3} ] }"
+ ", books:{count:2 , x:{buckets:[ {val:q,count:2},{val:w,count:2},{val:e,count:1} ]} }"
+ ", books2:{ buckets:[ {val:q,count:2},{val:w,count:2},{val:e,count:1} ] }"
+ ", pageof3:{count:1 , x:{buckets:[ {val:d,count:1},{val:e,count:1},{val:f,count:1} ]} }"
+ ", bookof22:{count:1 , x:{buckets:[ {val:B,count:1} ]} }"
+ ", missing_Parents:{count:0}"
+ ", missing_Children:{count:0}"
+ "}");
// no matches in base query
client.testJQ(
params(
"q",
"no_match_s:NO_MATCHES",
"json.facet",
"{ processEmpty:true,"
+ "pages:{ type:query, domain:{"
+ toChildren
+ "} }"
+ ",books:{ type:query, domain:{"
+ toParents
+ "} }"
+ "}"),
"facets=={ count:0" + ", pages:{count:0}" + ", books:{count:0}" + "}");
// test facet on children nested under terms facet on parents
client.testJQ(
params(
"q",
"*:*",
"json.facet",
"{"
+ "books:{ type:terms, field:book_s, facet:{ pages:{type:terms, field:v_t, domain:{"
+ toChildren
+ "}} } }"
+ "}"),
"facets=={ count:10"
+ ", books:{buckets:[{val:A,count:1,pages:{buckets:[]}}"
+ " ,{val:B,count:1,pages:{buckets:[{val:y,count:3},{val:x,count:2},{val:z,count:2}]}}"
+ " ,{val:C,count:1,pages:{buckets:[{val:x,count:1},{val:y,count:1},{val:z,count:1}]}}"
+ " ,{val:D,count:1,pages:{buckets:[]}}"
+ "] }"
+ "}");
// test filter after join
client.testJQ(
params(
p,
"q",
"*:*",
"json.facet",
"{ "
+ "pages1:{type:terms, field:v_t, domain:{"
+ toChildren
+ ", filter:'*:*'} }"
+ ",pages2:{type:terms, field:v_t, domain:{"
+ toChildren
+ ", filter:'-id:3.1'} }"
+ ",books:{type:terms, field:v_t, domain:{"
+ toParents
+ ", filter:'*:*'} }"
+ ",books2:{type:terms, field:v_t, domain:{"
+ toParents
+ ", filter:'id:2'} }"
+ "}"),
"facets=={ count:10"
+ ", pages1:{ buckets:[ {val:y,count:4},{val:x,count:3},{val:z,count:3} ] }"
+ ", pages2:{ buckets:[ {val:y,count:4},{val:z,count:3},{val:x,count:2} ] }"
+ ", books:{ buckets:[ {val:q,count:2},{val:w,count:2},{val:e,count:1} ] }"
+ ", books2:{ buckets:[ {val:q,count:1}, {val:w,count:1} ] }"
+ "}");
// test other various ways to get filters
client.testJQ(
params(
p,
"q",
"*:*",
"f1",
"-id:3.1",
"f2",
"id:2",
"json.facet",
"{ "
+ "pages1:{type:terms, field:v_t, domain:{"
+ toChildren
+ ", filter:[]} }"
+ ",pages2:{type:terms, field:v_t, domain:{"
+ toChildren
+ ", filter:{param:f1} } }"
+ ",books:{type:terms, field:v_t, domain:{"
+ toParents
+ ", filter:[{param:q},{param:missing_param}]} }"
+ ",books2:{type:terms, field:v_t, domain:{"
+ toParents
+ ", filter:[{param:f2}] } }"
+ "}"),
"facets=={ count:10"
+ ", pages1:{ buckets:[ {val:y,count:4},{val:x,count:3},{val:z,count:3} ] }"
+ ", pages2:{ buckets:[ {val:y,count:4},{val:z,count:3},{val:x,count:2} ] }"
+ ", books:{ buckets:[ {val:q,count:2},{val:w,count:2},{val:e,count:1} ] }"
+ ", books2:{ buckets:[ {val:q,count:1}, {val:w,count:1} ] }"
+ "}");
}
@Test
public void testFacetValueTypes() throws Exception {
doFacetValueTypeValidation(Client.localClient());
}
@Test
public void testFacetValueTypesDistrib() throws Exception {
initServers();
Client client = servers.getClient(random().nextInt());
client
.queryDefaults()
.set("shards", servers.getShards())
.set("debugQuery", Boolean.toString(random().nextBoolean()));
doFacetValueTypeValidation(client);
}
private void doFacetValueTypeValidation(Client client) throws Exception {
indexSimple(client);
client.testXQ(
params(
"q",
"*:*",
"rows",
"0",
"json.facet",
"{cat_s:{type:terms,field:cat_s,mincount:0,missing:true,allBuckets:true,numBuckets:true,limit:1}}"),
"/response/lst[@name='facets']/long[@name='count'][.=6]", // count
// total number of buckets
"/response/lst[@name='facets']/lst[@name='cat_s']/long[@name='numBuckets'][.=2]",
"*[count(/response/lst[@name='facets']/lst[@name='cat_s']/arr[@name='buckets']/lst)=1]", // no of entries
"/response/lst[@name='facets']/lst[@name='cat_s']/lst[@name='allBuckets']/long[@name='count'][.=5]", // allBuckets
"/response/lst[@name='facets']/lst[@name='cat_s']/lst[@name='missing']/long[@name='count'][.=1]", // missing
"/response/lst[@name='facets']/lst[@name='cat_s']/arr[@name='buckets']/lst[1]/str[@name='val'][.='B']", // facet value
"/response/lst[@name='facets']/lst[@name='cat_s']/arr[@name='buckets']/lst[1]/long[@name='count'][.='3']" // facet count
);
// aggregations types for string
client.testXQ(
params(
"q",
"*:*",
"rows",
"0",
"json.facet",
"{unique:'unique(cat_s)',hll:'hll(cat_s)',vals:'countvals(cat_s)',missing:'missing(cat_s)'}"),
"/response/lst[@name='facets']/long[@name='count'][.=6]", // count
"/response/lst[@name='facets']/long[@name='unique'][.=2]", // unique
"/response/lst[@name='facets']/long[@name='hll'][.=2]", // hll
"/response/lst[@name='facets']/long[@name='vals'][.=5]", // values
"/response/lst[@name='facets']/long[@name='missing'][.=1]" // missing
);
// aggregations types for number
client.testXQ(
params(
"q",
"*:*",
"rows",
"0",
"json.facet",
"{unique:'unique(num_i)',hll:'hll(num_i)',vals:'countvals(num_i)',missing:'missing(num_i)'}"),
"/response/lst[@name='facets']/long[@name='count'][.=6]", // count
"/response/lst[@name='facets']/long[@name='unique'][.=4]", // unique
"/response/lst[@name='facets']/long[@name='hll'][.=4]", // hll
"/response/lst[@name='facets']/long[@name='vals'][.=5]", // values
"/response/lst[@name='facets']/long[@name='missing'][.=1]" // missing
);
// aggregations types for multi-valued number
client.testXQ(
params(
"q",
"*:*",
"rows",
"0",
"json.facet",
"{unique:'unique(num_is)',hll:'hll(num_is)',vals:'countvals(num_is)',missing:'missing(num_is)'}"),
"/response/lst[@name='facets']/long[@name='count'][.=6]", // count
"/response/lst[@name='facets']/long[@name='unique'][.=7]", // unique
"/response/lst[@name='facets']/long[@name='hll'][.=7]", // hll
"/response/lst[@name='facets']/long[@name='vals'][.=9]", // values
"/response/lst[@name='facets']/long[@name='missing'][.=1]" // missing
);
}
public void XtestPercentiles() {
AVLTreeDigest catA = new AVLTreeDigest(100);
catA.add(4);
catA.add(2);
AVLTreeDigest catB = new AVLTreeDigest(100);
catB.add(-9);
catB.add(11);
catB.add(-5);
AVLTreeDigest all = new AVLTreeDigest(100);
all.add(catA);
all.add(catB);
System.out.println(str(catA));
System.out.println(str(catB));
System.out.println(str(all));
// 2.0 2.2 3.0 3.8 4.0
// -9.0 -8.2 -5.0 7.800000000000001 11.0
// -9.0 -7.3999999999999995 2.0 8.200000000000001 11.0
}
private static String str(AVLTreeDigest digest) {
StringBuilder sb = new StringBuilder();
for (double d : new double[] {0, .1, .5, .9, 1}) {
sb.append(" ").append(digest.quantile(d));
}
return sb.toString();
}
/** test code to ensure TDigest is working as we expect. */
public void XtestTDigest() {
AVLTreeDigest t1 = new AVLTreeDigest(100);
t1.add(10, 1);
t1.add(90, 1);
t1.add(50, 1);
System.out.println(t1.quantile(0.1));
System.out.println(t1.quantile(0.5));
System.out.println(t1.quantile(0.9));
assertEquals(t1.quantile(0.5), 50.0, 0.01);
AVLTreeDigest t2 = new AVLTreeDigest(100);
t2.add(130, 1);
t2.add(170, 1);
t2.add(90, 1);
System.out.println(t2.quantile(0.1));
System.out.println(t2.quantile(0.5));
System.out.println(t2.quantile(0.9));
AVLTreeDigest top = new AVLTreeDigest(100);
t1.compress();
ByteBuffer buf = ByteBuffer.allocate(t1.byteSize()); // upper bound
t1.asSmallBytes(buf);
byte[] arr1 = Arrays.copyOf(buf.array(), buf.position());
ByteBuffer rbuf = ByteBuffer.wrap(arr1);
top.add(AVLTreeDigest.fromBytes(rbuf));
System.out.println(top.quantile(0.1));
System.out.println(top.quantile(0.5));
System.out.println(top.quantile(0.9));
t2.compress();
ByteBuffer buf2 = ByteBuffer.allocate(t2.byteSize()); // upper bound
t2.asSmallBytes(buf2);
byte[] arr2 = Arrays.copyOf(buf2.array(), buf2.position());
ByteBuffer rbuf2 = ByteBuffer.wrap(arr2);
top.add(AVLTreeDigest.fromBytes(rbuf2));
System.out.println(top.quantile(0.1));
System.out.println(top.quantile(0.5));
System.out.println(top.quantile(0.9));
}
public void XtestHLL() {
HLLAgg.HLLFactory fac = new HLLAgg.HLLFactory();
HLL hll = fac.getHLL();
hll.addRaw(123456789);
hll.addRaw(987654321);
}
/** atomically resets the actual AtomicLong value matches the expected and resets it to 0 */
private static void assertEqualsAndReset(String msg, long expected, AtomicLong actual) {
final long current = actual.getAndSet(0);
assertEquals(msg, expected, current);
}
/** atomically resets the actual AtomicLong value matches the expected and resets it to 0 */
private static void assertEqualsAndReset(long expected, AtomicLong actual) {
final long current = actual.getAndSet(0);
assertEquals(expected, current);
}
}