blob: 11ad7f689e280fe7d868fefd0a693a384ea4c1a9 [file] [log] [blame]
###############################################################################
# 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.
###############################################################################
##
## Required Properties
##
#dataInputFormat -- required -- 'base', 'elasticsearch', or 'standalone' -- Specify the input format
pir.dataInputFormat=
#outputFile -- required -- Fully qualified name of output file in hdfs
pir.outputFile=
#platform -- required -- 'mapreduce', 'spark', or 'standalone'
#Processing platform technology for the responder
platform=
#queryInput -- required -- Fully qualified dir in hdfs of Query files
pir.queryInput=
##
## Optional Args - Leave empty if not using/not changing default values
##
#inputData -- required if baseInputFormat = 'base'
#Fully qualified name of input file/directory in hdfs; used if inputFormat = 'base'
#pir.inputData=
#dataSchemas -- optional -- Comma separated list of data schema file names to load
#responder.dataSchemas=
#querySchemas -- optional -- Comma separated list of query schema file names to load
#responder.querySchemas=
#allowAdHocQuerySchemas -- 'true' or 'false'
#If true, allows embedded QuerySchemas for a query.
#Defaults to 'false'
#pir.allowEmbeddedQuerySchemas=
#colMultReduceByKey -- 'true' or 'false' -- Spark only
#If true, uses reduceByKey in performing column multiplication; if false, uses groupByKey -> reduce
#Defaults to 'false'
#pir.colMultReduceByKey=
#baseInputFormat -- required if baseInputFormat = 'base'
#Full class name of the InputFormat to use when reading in the data - must extend BaseInputFormat
#pir.baseInputFormat=
#esQuery -- required if baseInputFormat = 'elasticsearch' -- ElasticSearch query
#if using 'elasticsearch' input format
#pir.esQuery=
#esResource -- required if baseInputFormat = 'elasticsearch'
#Requires the format <index>/<type> : Elasticsearch resource where data is read and written to
#pir.esResource=
#useHDFSLookupTable -- 'true' or 'false' - Whether or not to generate and use the
#hdfs lookup table for modular exponentiation
#Defaults to 'false'
#pir.useHDFSLookupTable=
#baseQuery -- ElasticSearch-like query if using 'base' input format -
#used to filter records in the RecordReader
#Defaults to ?q=*
#pir.baseQuery=
#limitHitsPerSelector -- 'true' or 'false'
#Whether or not to limit the number of hits per selector
#Defaults to 'true'
#pir.limitHitsPerSelector=
#mapreduceMapJavaOpts -- Amount of heap (in MB) to allocate per map task
#Defaults to -Xmx2800m
#mapreduce.map.java.opts=
#mapreduceMapMemoryMb -- Amount of memory (in MB) to allocate per map task
#Defaults to 3000
#mapreduce.map.memory.mb=
#mapreduceReduceJavaOpts
#Amount of heap (in MB) to allocate per reduce task
#Defaults to -Xmx2800m
#mapreduce.reduce.java.opts=
#mapreduceReduceMemoryMb
#Amount of memory (in MB) to allocate per reduce task
#Defaults to 3000
#mapreduce.reduce.memory.mb=
#stopListFile -- optional (unless using StopListFilter) -- Fully qualified file in hdfs
#containing stoplist terms; used by the StopListFilter
#pir.stopListFile=
#useLocalCache -- 'true' or 'false'
#Whether or not to use the local cache for modular exponentiation
#Defaults to 'true'
#pir.useLocalCache=
#useModExpJoin -- 'true' or 'false' -- Spark only
#Whether or not to pre-compute the modular exponentiation table and join it to the data
#partitions when performing the encrypted row calculations
#Defaults to 'false'
#pir.useModExpJoin=
#numReduceTasks -- optional -- Number of reduce tasks
#pir.numReduceTasks=
#numColMultPartitions -- optional, Spark only
#Number of partitions to use when performing column multiplication
#pir.numColMultPartitions=
#maxHitsPerSelector -- optional -- Max number of hits encrypted per selector
#pir.maxHitsPerSelector=
#dataParts -- optional -- Number of partitions for the input data
#pir.numDataPartitions=
#numExpLookupPartitions -- optional -- Number of partitions for the exp lookup table
#pir.numExpLookupPartitions=