Solr as a Spark SQL Datasource

Solr as a Spark SQL Datasource
Kiran Chitturi,
Lucidworks

Solr & Spark
• A few interesting things about Spark
• Overview of SparkSQL and DataFrames
• Solr as a SparkSQL DataSource in depth
• Use Lucene for text analysis in ML pipelines
• Example Use Case: Lucidworks Fusion and Spark

The standard for
enterprise search. of Fortune 500
uses Solr.
90%

What’s interesting about Spark?
• Wealth of overview / getting started resources on the Web
➢ Start here -> https://spark.apache.org/
➢ Should READ! https://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf
• Faster, more modernized alternative to MapReduce
➢ Spark running on Hadoop sorted 100TB in 23 minutes (3x faster than Yahoo’s previous record while using10x less
computing power)
• Unified platform for Big Data
➢ Great for iterative algorithms (PageRank, K-Means, Logistic regression) & interactive data mining
➢ Runs on YARN, Mesos, and plays well with HDFS
• Nice API for Java, Scala, Python, R, and sometimes SQL … REPL interface too
• >14,200 Issues in JIRA, 1000+ code contributors, 2.0 coming soon!

Spark Components
Spark Core
Spark
SQL
Spark
Streaming
MLlib
(machine
learning)
GraphX
(BSP)
Hadoop YARN Mesos Standalone
HDFS
Execution
Model
The Shuffle Caching
components
engine
cluster
mgmt
Alluxio (formerly Tachyon)
languages Scala Java Python R
shared
memory

Physical Architecture (Standalone)
Spark Master (daemon)
Spark Slave (daemon)
my-spark-job.jar
(w/ shaded deps)
My Spark App
SparkContext
(driver)
• Keeps track of live workers
• Web UI on port 8080
• Task Scheduler
• Restart failed tasks
Spark Executor (JVM process)
Tasks
Executor runs in separate
process than slave daemon
Spark Worker Node (1...N of these)
Each task works on some partition of a
data set to apply a transformation or action
Cache
Losing a master prevents new
applications from being executed
Can achieve HA using ZooKeeper
and multiple master nodes
Tasks are assigned
based on data-locality
When selecting which node to execute a task on,
the master takes into account data locality
• RDD Graph
• DAG Scheduler
• Block tracker
• Shuffle tracker

Spark SQL
• DataSource API for reading from and writing to external data sources
• DataFrame is an RDD[Row] + schema
• Secret sauce is logical plan optimizer
• SQL or relational operators on DF
• JDBC / ODBC
• UDFs!
• Machine Learning Pipelines

Solr as a Spark SQL Data Source
• Read/write data from/to Solr as DataFrame
• Use Solr Schema API to access field-level metadata
• Push predicates down into Solr query constructs, e.g. fq clause
• Deep-paging, shard partitioning, intra-shard splitting, streaming results
// Connect to Solr
val opts = Map("zkhost" -> "localhost:9983", "collection" -> "nyc_trips")
val solrDF = sqlContext.read.format("solr").options(opts).load
// Register DF as temp table
solrDF.registerTempTable("trips")
// Perform SQL queries
sqlContext.sql("SELECT avg(tip_amount), avg(fare_amount) FROM trips").show()

Shard 1
Shard 2
socialdata
collection
Partition 1
(spark task)
solr.DefaultSource
Partition 2
(spark task)
Spark
Driver
App
ZooKeeper
Read collection metadata
(num shards, replica URLs, etc)
Table Scan Query:
q=*:*&rows=1000&
distrib=false&cursorMark=*
Results streamed back from Solr
Parallelizequeryexecution
sqlContext.load(“solr”,opts)
DataFrame
(schema + RDD<Row>)
Map("collection” -> “nyc_trips”,
“zkHost” -> “…”)
SolrRDD: Reading data from Solr into Spark

Shard 1
Shard 2
socialdata
collection
Partition 1
(spark task)
solr.DefaultSource
Partition 3
(spark task)
Spark
Driver
App
ZooKeeper
Read collection metadata
(num shards, replica URLs, etc)
Table Scan Query:
q=*:*&rows=1000&
distrib=false&cursorMark=*
&split_field:[aa TO ba}
Parallelizequeryexecution
sqlContext.load(“solr”,opts)
DataFrame
(schema + RDD<Row>)
Map(“collection” -> “nyc_trips”,
“zkHost” -> “…”)
SolrRDD: Reading data from Solr into Spark
Partition 2
(spark task) &split_field:[ba TO bz]
Partition 4
(spark task)
&split_field:[aa TO ac}
&split_field:[ac TO ae]

Data-locality Hint
• SolrRDD extends RDD[SolrDocument] (written in Scala)
• Give hint to Spark task scheduler about where data lives
override def getPreferredLocations(split: Partition): Seq[String] = {
// return preferred hostname for a Solr partition 
}
• Useful when Spark executor and Solr replicas live on same physical
host, as we do in Fusion
• Query to a shard has a “preferred” replica; can fallback to other
replicas if the preferred goes down (will be in 2.1)

Solr Streaming API for fast reads
• Contributed to spark-solr by Bloomberg team (we PRs)
• Extremely fast “table scans” over large result sets in Solr
• Relies on a column-oriented data structure in Lucene: docValues
• DocValues help speed up faceting and sorting too!
• Coming soon! Push SQL predicates down into Solr’s Parallel SQL
engine available in Solr 6.x

Writing to Solr (aka indexing)
• Cloud-aware client sends updates to shard leaders in parallel
• Solr Schema API used to create fields on-the-fly using the DataFrame
schema
• Better parallelism than traditional approaches like Solr DIH
val dbOpts = Map(
"url" -> "jdbc:postgresql:mydb",
"dbtable" -> “schema.table",
"partitionColumn" -> "foo",
"numPartitions" -> "10")
val jdbcDF = sqlContext.read.format("jdbc").options(dbOpts).load
val solrOpts = Map("zkhost" -> "localhost:9983", "collection" -> "mycoll")
jdbcDF.write.format("solr").options(solrOpts).mode(SaveMode.Overwrite).save

Solr / Lucene Analyzers for Spark ML Pipelines
• Spark ML Pipeline provides nice API for defining stages to train / predict ML models
• Crazy idea ~ use battle-hardened Lucene for text analysis in Spark
• Pipelines support import/export (work in progress, more coming in Spark 2.0)
• Can try different text analysis techniques during cross-validation
DF
Spark ML Pipeline
Lucene
Analyzer
HashingTF
Standard
Scaler
Trained
Model
(SVM)
save
https://lucidworks.com/blog/2016/04/13/spark-solr-lucenetextanalyzer/

Fusion & Spark
• spark-solr 2.0.1 released, built into Fusion 2.4
• Users leave evidence of their needs & experience as they use your app
• Fusion closes the feedback loop to improve results based on user “signals”
• Train and serve ML Pipeline and mllib based Machine Learning models
• Run custom Scala “script” jobs in the background in Fusion
➢Complex aggregation jobs (see next slide)
➢Unsupervised learning (LDA topic modeling)
➢Re-train supervised models as new training data flows in

Scheduled Fusion job to compute stats for user sessions
val opts = Map("zkhost" -> "localhost:9983”, "collection" -> "apachelogs”)
var logEvents = sqlContext.read.format("solr").options(opts).load
logEvents.registerTempTable("logs”)
sqlContext.udf.register("ts2ms", (d: java.sql.Timestamp) => d.getTime)
sqlContext.udf.register("asInt", (b: String) => b.toInt)
val sessions = sqlContext.sql("""
|SELECT *, sum(IF(diff_ms > 30000, 1, 0))
|OVER (PARTITION BY clientip ORDER BY ts) session_id
|FROM (SELECT *, ts2ms(ts) - lag(ts2ms(ts))
|OVER (PARTITION BY clientip ORDER BY ts) as diff_ms FROM logs) tmp
""".stripMargin)
sessions.registerTempTable("sessions")
var sessionsAgg = sqlContext.sql("""
|SELECT concat_ws('||', clientip,session_id) as id,
| first(clientip) as clientip,
| min(ts) as session_start,
| max(ts) as session_end,
| (ts2ms(max(ts)) - ts2ms(min(ts))) as session_len_ms_l,
| count(*) as total_requests_l
|FROM sessions
|GROUP BY clientip,session_id
""".stripMargin)
sessionsAgg.write.format("solr").options(Map("zkhost" -> "localhost:9983", "collection" -> "apachelogs_signals_aggr"))
.mode(org.apache.spark.sql.SaveMode.Overwrite).save

Getting started with spark-solr
• Import package via maven
./bin/spark-shell --packages "com.lucidworks.solr:spark-solr:2.0.1"
• Build from source
git clone https://github.com/LucidWorks/spark-solr
cd spark-solr
mvn clean package -DskipTests
./bin/spark-shell --jars 2.1.0-SNAPSHOT.jar

Example : Deep paging via shards
// Connect to Solr
val opts = Map(
"zkhost" -> "localhost:9983",
"collection" -> "nyc_trips")
sqlContext.sql("SELECT * FROM trips LIMIT 2").show()

Example : Deep paging with intra shard splitting
// Connect to Solr
val opts = Map(
"collection" -> "nyc_trips",
"splits" -> "true")
sqlContext.sql("SELECT * FROM trips").count()

Example : Streaming API (/export handler)
// Connect to Solr
val opts = Map(
"collection" -> "nyc_trips")
sqlContext.sql("SELECT avg(tip_amount), avg(fare_amount) FROM
trips").show()

Performance test
• NYC taxi data (30 months - 91.7M rows)
• Dataset loaded in to AWS RDS instance (Postgres)
• 3 EC2 nodes of r3.2x large instances
• Solr and Spark instances co-located together
• Collection ‘nyc-taxi’ created with 6 shards, 1 replication
• Deployed using solr-scale-tk (https://github.com/LucidWorks/solr-scale-tk)
• Dataset link: https://github.com/toddwschneider/nyc-taxi-data
• More details: https://gist.github.com/kiranchitturi/
0be62fc13e4ec7f9ae5def53180ed181

• Query - simple aggregation query to calculate averages
• Streaming expressions took 2.3 mins across 6 tasks
• Deep paging took 20 minutes across 120 tasks
Query performance

Index performance
• 91.4M rows imported to Solr in 49 minutes
• Docs per second: 31K
• JDBC batch size: 5000
• Indexing batch size: 50000
• Partitions: 200

Wrap-up and Q & A
Download Fusion: http://lucidworks.com/fusion/download/
Feel free to reach out to me with questions:
kiran.chitturi@lucidworks.com / @chitturikiran

Spark Streaming: Nuts & Bolts
• Transform a stream of records into small, deterministic batches
✓ Discretized stream: sequence of RDDs
✓ Once you have an RDD, you can use all the other Spark libs (MLlib, etc)
✓ Low-latency micro batches
✓ Time to process a batch must be less than the batch interval time
• Two types of operators:
✓ Transformations (group by, join, etc)
✓ Output (send to some external sink, e.g. Solr)
• Impressive performance!
✓ 4GB/s (40M records/s) on 100 node cluster with less than 1 second latency (note: not indexing rate)
✓ Haven’t found any unbiased, reproducible performance comparisons between Storm / Spark

Spark Streaming Example: Solr as Sink
Twitter
./spark-submit --master MASTER --class com.lucidworks.spark.SparkApp spark-solr-1.0.jar
twitter-to-solr -zkHost localhost:2181 –collection social
Solr
JavaReceiverInputDStream<Status> tweets =
TwitterUtils.createStream(jssc, null, filters);
Various transformations / enrichments
on each tweet (e.g. sentiment analysis,
language detection)
JavaDStream<SolrInputDocument> docs = tweets.map(
new Function<Status,SolrInputDocument>() {
// Convert a twitter4j Status object into a SolrInputDocument
public SolrInputDocument call(Status status) {
SolrInputDocument doc = new SolrInputDocument();
…
return doc;
map()
class TwitterToSolrStreamProcessor
extends SparkApp.StreamProcessor
SolrSupport.indexDStreamOfDocs(zkHost, collection, 100, docs);
Slide Legend
Provided by Spark
Custom Java / Scala code
Provided by Lucidworks

Document Matching using Stored Queries
• For each document, determine which of a large set of stored queries
matches.
• Useful for alerts, alternative flow paths through a stream, etc
• Index a micro-batch into an embedded (in-memory) Solr instance and
then determine which queries match
• Matching framework; you have to decide where to load the stored
queries from and what to do when matches are found
• Scale it using Spark … need to scale to many queries, checkout Luwak

Document Matching using Stored Queries
Stored Queries
DocFilterContext
Twitter map()
Slide Legend
Provided by Spark
Custom Java / Scala code
Provided by Lucidworks
JavaReceiverInputDStream<Status> tweets =
TwitterUtils.createStream(jssc, null, filters);
JavaDStream<SolrInputDocument> docs = tweets.map(
new Function<Status,SolrInputDocument>() {
// Convert a twitter4j Status object into a SolrInputDocument
public SolrInputDocument call(Status status) {
SolrInputDocument doc = new SolrInputDocument();
…
return doc;
}});
JavaDStream<SolrInputDocument> enriched = 
SolrSupport.filterDocuments(docFilterContext, …);
Get queries
Index docs into an
EmbeddedSolrServer
Initialized from configs
stored in ZooKeeper
…
ZooKeeper
Key abstraction to allow
you to plug-in how to
store the queries and
what action to take when
docs match

RDD Illustrated: Word count
map(word => (word, 1))
Map words into
pairs with count of 1
(quick,1)
(brown,1)
(fox,1)
(quick,1)
(quick,1)
val file =
spark.textFile("hdfs://...")
HDFS
file RDD from HDFS
quick brown fox jumped …
quick brownie recipe …
quick drying glue …
………
file.flatMap(line => line.split(" "))
Split lines into words
quick
brown
fox
quick
quick
……
reduceByKey(_ + _)
Send all keys to same
reducer and sum
(quick,1)
(quick,1)
(quick,1)
(quick,3)
Shuffle
across
machine
boundaries
Executors assigned based on data-locality if possible, narrow transformations occur in same executor
Spark keeps track of the transformations made to generate each RDD
Partition 1
Partition 2
Partition 3
x
val file = spark.textFile("hdfs://...")
val counts = file.flatMap(line => line.split(" "))
.map(word => (word, 1))
.reduceByKey(_ + _)
counts.saveAsTextFile("hdfs://...")

Understanding Resilient Distributed Datasets (RDD)
• Read-only partitioned collection of records with fault-tolerance
• Created from external system OR using a transformation of another RDD
• RDDs track the lineage of coarse-grained transformations (map, join, filter, etc)
• If a partition is lost, RDDs can be re-computed by re-playing the transformations
• User can choose to persist an RDD (for reusing during interactive data-mining)
• User can control partitioning scheme

Physical Architecture
Spark Master (daemon)
Spark Slave (daemon)
my-spark-job.jar
(w/ shaded deps)
My Spark App
SparkContext
(driver)
• Keeps track of live workers
• Web UI on port 8080
• Task Scheduler
• Restart failed tasks
Spark Executor (JVM process)
Tasks
Executor runs in separate
process than slave daemon
Spark Worker Node (1...N of these)
Each task works on some partition of a
data set to apply a transformation or action
Cache
Losing a master prevents new
applications from being executed
Can achieve HA using ZooKeeper
and multiple master nodes
Tasks are assigned
based on data-locality
When selecting which node to execute a task on,
the master takes into account data locality
• RDD Graph
• DAG Scheduler
• Block tracker
• Shuffle tracker

Spark vs. Hadoop’s Map/Reduce
Operators File System Fault-Tolerance Ecosystem
Hadoop Map-Reduce HDFS
S3
Replicated
blocks
Java API
Pig
Hive
Spark filter, map,
flatMap, join,
groupByKey,
reduce,
sortByKey,
count, distinct,
top, cogroup,
etc …
HDFS
S3
Tachyon
Immutable RDD
lineage
Python / Scala /
Java API
SparkSQL
GraphX
MLlib
SparkR

Solr as a Spark SQL Datasource

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