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apache / lucene-solr / c2f26ac784945dca6d096b58f2d0e98196562894 / . / 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 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 com.carrotsearch.randomizedtesting.annotations.ParametersFactory;
import com.tdunning.math.stats.AVLTreeDigest;
import org.apache.lucene.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 {
systemSetPropertySolrDisableShardsWhitelist("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 {
systemClearPropertySolrDisableShardsWhitelist();
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;
@ParametersFactory
public static Iterable<Object[]> parameters() {
if (null != TEST_ONLY_ONE_FACET_METHOD) {
return Arrays.<Object[]>asList(new Object[] { TEST_ONLY_ONE_FACET_METHOD });
}
// wrap each enum val in an Object[] and return as Iterable
return () -> Arrays.stream(FacetField.FacetMethod.values())
.map(it -> new Object[]{it}).iterator();
}
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);
}
Collections.sort(idx, (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 testBehaviorEquivilenceOfUninvertibleFalse() 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 multi valued 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 equivilently to it's 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 equivilent to it's 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 of 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 of 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,$back)' }")
, "facets=={"
+ " count:5, "
+ " skg : { relatedness: 0.00699,"
+ " foreground_popularity: 0.33333,"
+ " background_popularity: 0.83333,"
+ " } }"
);
// simple single level facet w/skg stat & (re)sorting
for (String sort : Arrays.asList("sort:'index asc'",
"sort:'y desc'",
"sort:'z desc'",
"sort:'skg desc'",
"prelim_sort:'count desc', sort:'index asc'",
"prelim_sort:'count desc', sort:'y desc'",
"prelim_sort:'count desc', sort:'z desc'",
"prelim_sort:'count desc', sort:'skg desc'")) {
// the relatedness score of each of our cat_s values is (conviniently) also alphabetical order,
// (and the same order as 'sum(num_i) desc' & 'min(num_i) desc')
//
// So all of these re/sort options should produce identical output (since the num buckets is < limit)
// - Testing "index" sort allows the randomized use of "stream" processor as default to be tested.
// - Testing (re)sorts on other stats sanity checks code paths where relatedness() is a "defered" Agg
for (String limit : Arrays.asList(", ", ", limit:5, ", ", limit:-1, ")) {
// results shouldn't change regardless of our limit param"
assertJQ(req("q", "cat_s:[* TO *]", "rows", "0",
"fore", "where_s:NY", "back", "*:*",
"json.facet", ""
+ "{x: { type: terms, field: 'cat_s', "+sort + limit
+ " facet: { skg: 'relatedness($fore,$back)', y:'sum(num_i)', z:'min(num_i)' } } }")
, "facets=={count:5, x:{ buckets:["
+ " { val:'A', count:2, y:5.0, z:2, "
+ " skg : { relatedness: 0.00554, "
//+ " foreground_count: 1, "
//+ " foreground_size: 2, "
//+ " background_count: 2, "
//+ " background_size: 6,"
+ " foreground_popularity: 0.16667,"
+ " background_popularity: 0.33333, },"
+ " }, "
+ " { val:'B', count:3, y:-3.0, z:-5, "
+ " skg : { relatedness: 0.0, " // perfectly average and uncorrolated
//+ " foreground_count: 1, "
//+ " foreground_size: 2, "
//+ " background_count: 3, "
//+ " background_size: 6,"
+ " foreground_popularity: 0.16667,"
+ " background_popularity: 0.5 },"
+ " } ] } } "
);
// same query with a prefix of 'B' should produce only a single bucket with exact same results
assertJQ(req("q", "cat_s:[* TO *]", "rows", "0",
"fore", "where_s:NY", "back", "*:*",
"json.facet", ""
+ "{x: { type: terms, field: 'cat_s', prefix:'B', "+sort + limit
+ " facet: { skg: 'relatedness($fore,$back)', y:'sum(num_i)', z:'min(num_i)' } } }")
, "facets=={count:5, x:{ buckets:["
+ " { val:'B', count:3, y:-3.0, z:-5, "
+ " skg : { relatedness: 0.0, " // perfectly average and uncorrolated
//+ " foreground_count: 1, "
//+ " foreground_size: 2, "
//+ " background_count: 3, "
//+ " background_size: 6,"
+ " foreground_popularity: 0.16667,"
+ " background_popularity: 0.5 },"
+ " } ] } } "
);
}
}
// relatedness shouldn't be computed for allBuckets, but it also shouldn't cause any problems
for (String sort : Arrays.asList("sort:'y desc'",
"sort:'z desc'",
"sort:'skg desc'",
"sort:'index asc'",
"prelim_sort:'count desc', sort:'skg desc'")) {
// the relatedness score of each of our cat_s values is (conveniently) also alphabetical order,
// (and the same order as 'sum(num_i) desc' & 'min(num_i) desc')
//
// So all of these re/sort options should produce identical output (since the num buckets is < limit)
// - Testing "index" sort allows the randomized use of "stream" processor as default to be tested.
// - Testing (re)sorts on other stats sanity checks code paths where relatedness() is a "deferred" Agg
for (String limit : Arrays.asList(", ", ", limit:5, ", ", limit:-1, ")) {
// results shouldn't change regardless of our limit param"
assertJQ(req("q", "cat_s:[* TO *]", "rows", "0",
"fore", "where_s:NY", "back", "*:*",
"json.facet", ""
+ "{x: { type: terms, field: 'cat_s', allBuckets:true, "+sort + limit
+ " facet: { skg: 'relatedness($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 uncorrolated
//+ " 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 uncorrolated
//+ " 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 uncorrolated
//+ " 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 (conviniently) 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 "defered" 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,$back)', "
+" "+RelatednessAgg.SWEEP_COLLECTION+": "+sweep+" },"
+ " y:'sum(num_i)', "
+" z:'min(num_i)' } } }")
, "facets=={count:5, x:{ buckets:["
+ " { val:'A', count:2, y:5.0, z:2, "
+ " skg : { relatedness: 0.00554, "
+ " foreground_popularity: 0.16667,"
+ " background_popularity: 0.33333, },"
+ " }, "
+ " { val:'B', count:3, y:-3.0, z:-5, "
+ " skg : { relatedness: 0.0, " // perfectly average and uncorrolated
+ " foreground_popularity: 0.16667,"
+ " background_popularity: 0.5 },"
+ " } ] } } "
);
}
}
}
@Test
public void testRepeatedNumerics() throws Exception {
Client client = Client.localClient();
String field = "num_is"; // docValues of multi-valued points field can contain duplicate values... make sure they don't mess up our counts.
client.add(sdoc("id", "1", "cat_s", "A", "where_s", "NY", "num_d", "4", "num_i", "2", "val_b", "true", "sparse_s", "one", field,"0", field,"0"), null);
client.commit();
client.testJQ(params("q", "id:1", "field", field
, "json.facet", "{" +
"f1:{terms:${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"));
fieldLists.put("num_d", getAlternatives("num_f")); // num_d name is historical, which is why we map it to num_f alternatives so 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);
Client iclient = client;
/*** 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");
iclient.add(doc, null);
iclient.add(doc, null);
iclient.add(doc, null); // a couple of deleted docs
iclient.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);
iclient.add(sdoc("id", "3"), null);
iclient.commit();
iclient.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);
iclient.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);
iclient.commit();
iclient.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);
iclient.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 indepth tests of this in doTestPrelimSorting, but this let's us sanity check
// small responses with multiple templatized 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(${sparse_num_d})'} } " +
"}"
)
, "facets=={ 'count':6, " +
" f1:{ 'buckets':[{ val:'A', count:2, x:2.0 }, { val:'B', count:3, x:-9.0}]}" +
", f2:{ 'buckets':[{ val:'B', count:3, x:11.0 }, { val:'A', count:2, x:4.0 }]} " +
", f3:{ 'buckets':[{ val:'A', count:2, x:2 }, { val:'B', count:3, x:2 }]} " +
", f4:{ 'buckets':[{ val:'A', count:2, x:2 }, { val:'B', count:3, x:2 }]} " +
", f5:{ 'buckets':[{ val:'B', count:3, x:74.6666666666666 }, { val:'A', count:2, x:1.0 }]} " +
", f6:{ buckets:[{ val:-9.0, count:1, x:1 }]} " +
", f7:{ buckets:[{ val:B, count:3, x:3 },{ val:A, count:2, x:0 }]} " +
", f8:{ buckets:[{ val:A, count:2, x:2 },{ val:B, count:3, x:0 }]} " +
"}"
);
// test sorting by stat with function
client.testJQ(params(p, "q", "*:*"
, "json.facet", "{f1:{terms:{${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(${sparse_num_d},50)'} }} " +
"}"
)
, "facets=={ 'count':6, " +
" f1:{ 'buckets':[{ val:'A', count:2, n1:3.0 }, { val:'B', count:3, n1:-5.0}]}" +
", f2:{ 'buckets':[{ val:'B', count:3, n1:-5.0}, { val:'A', count:2, n1:3.0 }]}" +
", f3:{ 'buckets':[{ val:'A', count:2, n1:1.0}, { val:'B', count:3}]}" +
"}"
);
// test sorting by multiple percentiles (sort is by first)
client.testJQ(params(p, "q", "*:*"
, "json.facet", "{f1:{terms:{${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(${num_d},50,0,100)'} }} }"
)
, "facets=={ 'count':6, " +
" f1:{ 'buckets':[{ val:'A', count:2, n1:[3.0,2.0,4.0] }, { val:'B', count:3, n1:[-5.0,-9.0,11.0] }]}" +
", f2:{ 'buckets':[{ val:'B', count:3, n1:[-5.0,-9.0,11.0]}, { val:'A', count:2, n1:[3.0,2.0,4.0] }]} }"
);
// test sorting by count/index order
client.testJQ(params(p, "q", "*:*"
, "json.facet", "{f1:{terms:{${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(${sparse_num_d})'" +
" }"
)
, "facets=={ 'count':6, " +
"sum1:3.0, sumsq1:247.0, avg1:0.6, avg2:0.5, mind:-9.0, maxd:11.0" +
", numwhere:2, unique_num_i:4, unique_num_d:5, unique_date:5" +
", where_hll:2, hll_num_i:4, hll_num_d:5, hll_date:5" +
", med:2.0, perc:[-9.0,2.0,11.0], variance:49.04, stddev:7.002856560004639" +
", mini:-5, maxi:7, missing:4, vals:2" +
"}"
);
// stats at top level on multi-valued fields
client.testJQ(params(p, "q", "*:*", "myfield", "${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:2.537222891273055, variance:6.4375, median:0.0, perc:[-5.0,2.25,3.0], 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(${num_is}, 50)'} }} }"
)
, "facets=={ 'count':6, " +
" f1:{ 'buckets':[{ val:'B', count:3, n1: -0.50}, { val:'A', count:2, n1:1.0}]}" +
", f2:{ 'buckets':[{ val:'A', count:2, n1:1.0}, { val:'B', count:3, n1:-0.50 }]} }"
);
// test sorting by multi-valued field with domain query
client.testJQ(params(p, "q", "-id:*"
, "json.facet", "{f1:{terms:{${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:74.66666666666667, stddev:8.640987597877148, 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:1.0, stddev:1.0, 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:${super_s}, limit:1, facet:{x:'debug()'} }}}" // x should be deferred to 2nd phase
)
, "facets=={ 'count':6, " +
"f1:{ buckets:[{ val:batman, count:1, x:1}]} } "
);
assertEquals(1, DebugAgg.Acc.creates.get() - creates);
assertTrue( DebugAgg.Acc.resets.get() - resets <= 1);
assertTrue( DebugAgg.Acc.last.numSlots <= 2 ); // probably "1", but may be special slot for something. As long as it's not cardinality of the field
creates = DebugAgg.Acc.creates.get();
resets = DebugAgg.Acc.resets.get();
client.testJQ(params(p, "q", "*:*"
, "json.facet", "{f1:{terms:{${terms_method} field:${super_s}, limit:1, facet:{ x:'debug()'} , sort:'x asc' }}}" // sorting by x... must be done all at once in first phase
)
, "facets=={ 'count':6, " +
"f1:{ buckets:[{ val:batman, count:1, x:1}]}" +
" } "
);
assertEquals(1, DebugAgg.Acc.creates.get() - creates);
assertTrue( DebugAgg.Acc.resets.get() - resets == 0);
assertTrue( DebugAgg.Acc.last.numSlots >= 5 ); // all slots should be done in a single shot. there may be more than 5 due to special slots or hashing.
// When limit:-1, we should do most stats in first phase (SOLR-10634)
creates = DebugAgg.Acc.creates.get();
resets = DebugAgg.Acc.resets.get();
client.testJQ(params(p, "q", "*:*"
, "json.facet", "{f1:{terms:{${terms_method} field:${super_s}, limit:-1, facet:{x:'debug()'} }}}"
)
, "facets=="
);
assertEquals(1, DebugAgg.Acc.creates.get() - creates);
assertTrue( DebugAgg.Acc.resets.get() - resets == 0);
assertTrue( DebugAgg.Acc.last.numSlots >= 5 ); // all slots should be done in a single shot. there may be more than 5 due to special slots or hashing.
// Now for a numeric field
// When limit:-1, we should do most stats in first phase (SOLR-10634)
creates = DebugAgg.Acc.creates.get();
resets = DebugAgg.Acc.resets.get();
client.testJQ(params(p, "q", "*:*"
, "json.facet", "{f1:{terms:{${terms_method} field:${num_d}, limit:-1, facet:{x:'debug()'} }}}"
)
, "facets=="
);
assertEquals(1, DebugAgg.Acc.creates.get() - creates);
assertTrue( DebugAgg.Acc.resets.get() - resets == 0);
assertTrue( DebugAgg.Acc.last.numSlots >= 5 ); // all slots should be done in a single shot. there may be more than 5 due to special slots or hashing.
// But if we need to calculate domains anyway, it probably makes sense to calculate most stats in the 2nd phase (along with sub-facets)
creates = DebugAgg.Acc.creates.get();
resets = DebugAgg.Acc.resets.get();
client.testJQ(params(p, "q", "*:*"
, "json.facet", "{f1:{terms:{${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 to sanity 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.
*
* 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 sitaution, these clients will be the same, and we'll have the same
// total docs in our collection, but the numShardsWithData will be diff
// (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")) { // equivilent 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 compuation 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 choosen 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);
{ // sanity check how defered 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 defered 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 tie breaker 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 choosen 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=*:*)
* </p>
*/
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} ] }" +
"}"
);
}
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() throws Exception {
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);
}
/** atomicly resets the acctual AtomicLong value matches the expected and resets it to 0 */
private static final void assertEqualsAndReset(String msg, long expected, AtomicLong actual) {
final long current = actual.getAndSet(0);
assertEquals(msg, expected, current);
}
/** atomicly resets the acctual AtomicLong value matches the expected and resets it to 0 */
private static final void assertEqualsAndReset(long expected, AtomicLong actual) {
final long current = actual.getAndSet(0);
assertEquals(expected, current);
}
}