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Taboola's Transition to Apache Spark for Real-Time Analytics
- 1. Taboola's Road to Scale A Focus on Data & Apache Spark
- 2. Collaborative Filtering Bucketed Consumption Groups Geo Region-based Recommendations Context Metadata Social Facebook/Twitter API User Behavior Cookie Data Engine Focused on Maximizing CTR & Post Click Engagement
- 3. Largest Content Discovery and Monetization Network 60M monthly unique users 130B Monthly recommendations 1M+ sourced content providers 1M+ sourced content items
- 4. What Does it Mean? • Zero downtime allowed – Every single component is fault tolerant • 5 Data Centers across the globe • Tera-bytes of data / day (many billion events) • Data must be processed and analyzed in real time, for example: – Real-time, per user content recommendations – Real-time expenditure reports – Automated campaign management – Automated recommendation algorithms calibration – Real-time analytics
- 5. Taboola 2007 • Events and logs (rawdata) written directly to DB • Recs Are read from DB • Crashed when CNN launched Frontend RecServer
- 6. Taboola 2007.5 • Same as before, but without direct write to DB • Switching to bulk load • But – Very Basic Reporting, not scalable Frontend Bulk Load RecServer
- 7. Taboola 2008 • Introduced a semi realtime events parsing services: Session Parser and Session Analyzer • Divided analysis work by unit (session) • Files were pushed from RecServer(s) to Backend processing • Files are gzip textual INSERT statements • But – not real time enough Frontend NFS Backend RecServer SessionParser SessionAnalyzer Write Summarized Data Write rawdata Read session files Read rawdata Write session files
- 8. Taboola 2010 • Made a leap towards real- time stream processing • Unified Session Parser and Session Analyzer to an in- memory service (without going through disk) • Made dramatic optimization to memory allocation and data models • Failure safe architecture - can endure data delays, front-end servers’ malfunction • No direct DB access - key for performance, only using bulk loading for loading hourly data Frontend NFS Backend RecServer Session Parser + Analyzer Write Hourly Data (Bulk Loading) Write rawdata Read rawdata
- 9. Taboola 2011-2013 • Roughly same architecture • Increasing backend growth by scaling in (monster machines) • Introduced real-time analyzers • Introduced sharding • Moved to lsync based file sync • Introduced Top Reports capabilities Frontend Lsync Backend RecServer Session Parser + Analyzer Write Hourly Data (Bulk Loading) Write rawdata Read rawdata
- 10. Taboola 2014 • Spark as the distributed engine for data analysis (and distributed computing in general) • All critical data path already moved to Spark • New data modelling based on ProtoStuff(Buf) • Easily scalable • Easy ad hoc analysis/research
- 11. About Spark • Open Sourced • Apache top level project (since Feb. 19th) • DataBricks - A commercial company that supports it • Hadoop-compatible computing engine • Can run side-by-side with Hadoop/Hive on the same data • Drastically faster than Hadoop through in-memory computing • Multiple H/A options - standalone cluster, Apache mesos and ZooKeeper or YARN
- 12. • With over 100 developers and 25 companies, one of the most active communities in big data Spark Development Community Comparison: Storm (48), Giraph (52), Drill (18), Tez (12) Past 6 months: more active devs than Hadoop MapReduce!
- 15. Spark API • Simple to write through easy APIs in Java, Scala and Python • The same analytics code can be used for both streaming data and offline data processing
- 16. Spark Key Concepts Resilient Distributed Datasets • Collections of objects spread across a cluster, stored in RAM or on Disk • Built through parallel transformations • Automatically rebuilt on failure • Immutable Operations • Transformations (e.g. map, filter, groupB y) • Actions (e.g. count, collect, save ) Write programs in terms of transformations on distributed datasets
- 17. Load error messages from a log into memory, then interactively search for various patterns Example: Log Mining lines = spark.textFile(“hdfs://...”) errors = lines.filter(lambda s: s.startswith(“ERROR”)) messages = errors.map(lambda s: s.split(“t”)[2]) messages.cache() messages.filter(lambda s: “mysql” in s).count() messages.filter(lambda s: “php” in s).count() . . . Base RDD Transformed RDD Full-text search of Wikipedia • 60GB on 20 EC2 machine • 0.5 sec vs. 20s for on-disk
- 18. • General task graphs • Automatically pipelines functions • Data locality aware • Partitioning aware to avoid shuffles Task Scheduler = cached partition= RDD reduceByKey filter groupBy Stage 3 Stage 1 Stage 2 A: B: C: D: E: F: map
- 19. • Spark runs as a library in your program (1 instance per app) • Runs tasks locally or on cluster – Mesos, YARN or standalone mode • Accesses storage systems via Hadoop InputFormat API – Can use HBase, HDFS, S3, … Software Components Your application SparkContext Local threads Cluster manager Worker Spark executor Worker Spark executor HDFS or other storage
- 20. System Architecture & Data Flow @ Taboola Driver + ConsumersSpark Cluster MySQL Cluster FE Servers C* Cluster FE Servers
- 21. Execution Graph @ Taboola • Data start point (dates, etc)rdd1 = Context.parallize([data]) • Loading data from external sources (Cassandra, MySQL, etc) rdd2 = rdd1.mapPartitions(loadfunc()) • Aggregating the data and storing results rdd3 = rdd2.reduceByKey(reduceFunc()) • Saving the results to a DBrdd4 = rdd3.mapPartitions(saverfunc()) • Executing the above graph by forcing an output operation rdd4.count()
- 22. Cassandra as a Distributed Storage • Event Log Files saved as blobs to a dedicated keyspace • C* Tables holding the Event Log Files are partitioned by day – new Table per day. This way, it is easier for maintenance and simpler to load into Spark • Using Astyanax driver + CQL3 – Recipe to load all keys of a table very fast (hundred of thousands / sec) – Split by keys and then load data by key in batches – in parallel partitions • Wrote hadoop InputFormat that supports loading this into a lines RDD<String> – The DataStax InputFormat had issues and at the time was not formally supported • Worked well, but ended up not using it – instead using mapPartitions – Very simple, no overhead, no need to be tied to hadoop – Will probably use the InputFormat when we deploy a Shark solution • Plans to open source all this Key (String) Data (blob) GUID (originally log file name) Gzipped file GUID Gzipped file … … userevent_2014-02-19 Key (String) Data (blob) GUID (originally log file name) Gzipped file GUID Gzipped file … … userevent_2014-02-20
- 23. Sample – Click Counting for Campaign Stopping 1. mapPartitions – mapping from strings to objects with a pre designed click key 2. reduceByKey – removing duplicate clicks (see next slide) 3. Map – switch keys to a campaign+day key 4. reduceByKey – aggregate the data by campaign+day
- 24. Campaign Stopping – Removing Dup Clicks • When more than 1 click found from the same user on the same item, leave only the oldest • Using accumulators to track duplicate numbers • Notice – not Spark specific
- 25. Our Deployment • 16 nodes, each- – 24 cores – 256G Ram – 6 1TB SSD Disks – JBOD configuration – 10G Ethernet • Total Cluster Power – 4096GB Ram – 384 CPUs – 96 TB storage – (effective space is less, Cassandra Keyspaces defined with replication factor 3) • Symmetric Deployment – Mesos + Spark – Cassandra • More – Rabbit MQ on 3 nodes – ZooKeeper on 3 nodes – MySQL cluster outside this cluster • Loads & processes ~1 Tera Bytes (unzipped data) in ~3 minutes
- 26. Things that work well with Spark (from our experience) • Very easy to code complex jobs – Harder than SQL, but better than other Map Reduce options – Simple concepts, “small” API • Easy to Unit Test – Runs in local mode, so ideal for micro E2E tests – Each mapper/reducer can be unit tested without Spark – if you do not use anonymous classes • Very resilient • Can read/write to/from any data source, including RDBMS, Cassandra, HDFS, local files, etc. • Great monitoring • Easy to deploy & upgrade • Blazing fast
- 27. Things that do not work that well (from our experience) • Long (endless) running tasks require some workarounds – Temp files - Spark creates a lot of files in spark.local.dir, requires periodic cleanup – Use spark.cleaner.ttl for long running tasks • Spark Streaming – not fully mature when we tested – Some end cases can cause loss of data – Sliding window / batch model does not fit our needs • We always load some history to deal with late arriving data • State management left to the user and not trivial – BUT – we were able to easily implement a bullet proof home grown, near real time, streaming solution with minimal amount of code
- 28. General / Optimization Tips • Use Spark Accumulators to collect and report operational data • 10G Ethernet • Multiple SSD disks per node, JBOD configuration • A lot of memory for the cluster
- 29. Technologies Taboola Uses for Spark • Spark – computing cluster • Mesos – cluster resource manager – Better resource allocation (coarse grained) for Spark • ZooKeeper – distributed coordination – Enables multi master for mesos & spark • Cassandra – Distributed Data Store • Monitoring – http://metrics.codahale.com/
- 30. Attributions Many of the general Spark slides were taken from the DataBricks Spark Summit 2013 slides. There are great materials at: https://spark.apache.org/ http://spark-summit.org/summit-2013/
Editor's Notes
- One of the most exciting things you’ll findGrowing all the timeNASCAR slideIncluding several sponsors of this event are just starting to get involved…If your logo is not up here, forgive us – it’s hard to keep up!
- RDD Colloquially referred to as RDDs (e.g. caching in RAM)Lazy operations to build RDDs from other RDDsReturn a result or write it to storage
- Add “variables” to the “functions” in functional programming
- NOT a modified versionof Hadoop