###############################################################################
# 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=

#One of the following two options is required - launcher prefered

#launcher -- required -- full class name of a class implementing ResponderPlugin
#ie. org.apache.pirk.responder.wideskies.standalone.StandaloneResponderPluginProcessing platform technology for the responder
#launcher=

#platform -- required -- 'mapreduce', 'spark', 'sparkstreaming', 'standalone', or 'storm'
#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=

##Props for Spark Streaming

#batchSeconds - optional - Batch size (in seconds) for Spark Streaming - defaults to 30 sec
#pir.sparkstreaming.batchSeconds=

#windowLength - optional - Window size (in seconds) for Spark Streaming - defaults to 60 sec
#pir.sparkstreaming.windowLength=

#queueStream - optional - Use queue stream for Spark Streaming - defaults to false
#pir.sparkstreaming.useQueueStream=

#pir.sparkstreaming.maxBatches - optional - Spark Streaming - Max number of batches to process 
#defaults to -1 (no maximum)
#pir.sparkstreaming.maxBatches=

#spark.streaming.stopGracefullyOnShutdown - Spark Streaming - Whether or not to stop 'gracefully' during shutdown
#default is false
#spark.streaming.stopGracefullyOnShutdown=

 ##Properties for Kafka
 #kafka.topic = topicName
 #kafka.clientId = pirk_spout

 # Kafka Zookeepers
 #kafka.zk = localhost:2181
 # Read from beginning of Kafka topic on startup
 #kafka.forceFromStart = false


 ##Properties for Storm
 #storm.topoName = pir
 #storm.workers = 1
 #storm.numAckers = 1
 #storm.maxSpoutPending=10
 #storm.worker.heapMemory=6000
 #storm.componentOnheapMem= 600.0

 # This should be set to the number of Kafka partitions
 #storm.spout.parallelism = 1

 #storm.hashbolt.parallelism = 1
 #storm.encrowcalcbolt.parallelism = 1
 # This bolt is most computationally expensive and should have the highest value
 #storm.enccolmultbolt.parallelism = 2

 # These may be useful for tuning
 #storm.executor.receiveBufferSize = 1024
 #storm.executor.sendBufferSize = 1024
 #storm.transferBufferSize = 8

 # Frequency with which PIR matrix elements are flushed out
 #storm.encrowcalcbolt.ticktuple = 60

 # Design configurations:
 # Hashbolt emits individual tuples for each data partition when splitPartitions =true
 # emits the batch of data partitions for a record in a single tuple when =false
 #storm.splitPartitions = true
 # A task running EncColMultBolt will only be responsible for multiplying a subset of the row
 # for any individual column when saltColumns = true
 # All multiplication for a single column is done on a single EncColMultBolt instance when = false
 #storm.saltColumns = true
 # Only makes sense to tune if saltColumns=true
 #storm.rowDivs = 1