Open Source LinkedIn Analytics Pipeline - BOSS 2016 (VLDB)


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How to setup and operate an analytics pipeline using LinkedIn open source technologies (Gobblin, Pinot, Kafka, Hadoop).

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  • Detail what happens to an event at Linkedin,
    This presentation will mostly focus on Gobblin and Pinot for the purpose of analytics.
  • ~25 minutes
  • ~25 minutes
  • Open Source LinkedIn Analytics Pipeline - BOSS 2016 (VLDB)

    1. 1. ©2014 LinkedIn Corporation. All Rights Reserved.©2014 LinkedIn Corporation. All Rights Reserved.
    2. 2. Open Source Analytics Pipeline at LinkedIn Issac Buenrostro Jean-François Im BOSS Workshop, 2016
    3. 3. Outline 1 Overview of analytics at LinkedIn 2 Gobblin 3 Pinot 4 Demo 5 Operating an analytics pipeline in production 3
    4. 4. 4
    5. 5. LinkedIn in Numbers 5 Members: 450m+ Number of datasets: 10k+ Data volume generated per day: 100TB+ Total accumulated data: 20PB+ Multiple datacenters Thousands of nodes per Hadoop cluster
    6. 6. Analytics at LinkedIn 6 Kafka Tracking External Database Gobblin HDFS Pinot Visualize Apps Reports
    7. 7. In This Workshop 7 Kafka REST File System Query App
    8. 8. What is Gobblin? Universal data ingestion framework 9
    9. 9. Gobblin Architecture 10
    10. 10. Sample Use Cases 1 Stream dumps (e.g. Kafka -> HDFS) 2 Snapshot dumps (e.g. Oracle, Salesforce -> HDFS) 3 Stream loading (e.g. HDFS -> Kafka) 4 Data cleaning (HDFS -> HDFS purging) 5 File download/copy (x-cluster replication, FTP/SFTP download) 11
    11. 11. Features 12 1. Pluggable sources, converters, quality checkers, writers. 2. Run on single node, Gobblin managed cluster, AWS, YARN (as MR or standalone YARN app). 3. Single Gobblin instance for multiple sources / sinks. 4. Quick start using templates for most common jobs. 5. Other Gobblin suite tools: metrics, retention, configuration management, data compaction.
    12. 12. Gobblin at LinkedIn 1 In production since 2014 2 ~20 different sources: Kafka, OLTP, HDFS, SFTP, Salesforce, MySQL, etc. 3 Process >100 TB per day 4 Process 10,000+ different datasets with custom configurations 5 Configuration, retention, metrics, compaction handled by Gobblin suite 13
    13. 13. P not 14
    14. 14. What is Pinot? 15 • Distributed near-realtime OLAP datastore • Horizontally scalable for larger data volumes and query rates • Offers a SQL query interface • Can index and combine data pushed from offline data sources (eg. Hadoop) and realtime data sources (eg. Kafka) • Fault tolerant, no single point of failure
    15. 15. Pinot at LinkedIn 1 Over 50 different use cases (eg. “Who viewed my profile?”) 2 Several thousands of queries per second over billions of rows across multiple data centers 3 Operates 24x7 with no downtime for maintenance 4 The de facto data store for site-facing analytics at Linkedin 16
    16. 16. Pinot at Linkedin: Who Viewed My Profile? 17
    17. 17. Pinot Design Limitations 18 1. Pinot is designed for analytical workloads (OLAP), not transactional ones (OLTP) 2. Data in Pinot is immutable (eg. no UPDATE statement), though it can be overwritten in bulk 3. Realtime data is append-only (can only load new rows) 4. There is no support for JOINs or subselects 5. There are no UDFs for aggregation (work in progress)
    18. 18. Demo
    19. 19. How to run the demos back home 20 • Since we cover a lot of material during these demos, we’ll make the VM used for these demos available after the tutorial. This way you can focus on understanding what is demonstrated instead of trying to follow exactly what is being typed by the presenters. • You can grab a copy of the VM after the tutorial at pinot-demo-vm.tar.gz or in person after the tutorial if you want to avoid downloading over the hotel Wi-Fi
    20. 20. Gobblin Demo Outline 21 1. Setting up Gobblin 2. Kafka to file system ingest 3. Wikipedia to Kafka ingest from scratch 4. Metrics and events 5. Other running modes
    21. 21. Gobblin Setup 22 Download binary: Or download sources and build: ./gradlew assemble Find tarball at build/gobblin-distribution/distributions Untar, will generate a directory gobblin-dist
    22. 22. Gobblin Startup 23 cd gobblin-dist export JAVA_HOME=<java-home> mkdir $HOME/gobblin-jobs mkdir $HOME/gobblin-workspace bin/ --conf $HOME/gobblin- jobs/ --workdir $HOME/gobblin-workspace/ start
    23. 23. Gobblin Directory Layout 24 gobblin-dist/ Gobblin binaries and scripts |--- bin/ Startup scripts |--- conf/ Global configuration files |--- lib/ Classpath jars |--- logs/ Execution log files gobblin-workspace/ Workspace for Gobblin |--- locks/ Locks for each job |--- state-store/ Stores watermarks and failed work units |--- task-output/ Staging area for job output gobblin-jobs/ Place job configuration files here |--- job.pull A job configuration
    24. 24. Running a job 25 1. Place *.pull file in gobblin-jobs/ 2. New and modified files automatically found and will start executing. 3. Can provide cron-style schedule, or if absent, job will run once. (Per Gobblin instance)
    25. 25. Kafka Puller Job 26 gobblin-jobs/Kafka-puller.pull # Template to use job.template=templates/gobblin-kafka.template # Schedule in cron format job.schedule=0 0/15 * * * ? # every 15 minutes # Job configuration topics=test # Can override brokers # kafka.brokers="localhost:9092” Pull records from Kafka topic (default at localhost), write them to gobblin-jobs/job-output in plain text.
    26. 26. Kafka Puller Job – Json to Avro 27 gobblin-jobs/kafka-puller-jsontoavro.pull job.template=templates/gobblin-kafka.template job.schedule=0 0/1 * * * ? topics=jsonDate converter.classes=gobblin.converter.SchemaInjector,gobblin.converter.json.JsonStringToJ sonIntermediateConverter,gobblin.converter.avro.JsonIntermediateToAvroConverter gobblin.converter.schemaInjector.schema=<schema> writer.builder.class=gobblin.writer.AvroDataWriterBuilder writer.output.format=AVRO # Uncomment for partitioning by date # writer.partition.columns=timestamp # writer.partitioner.class=gobblin.writer.partitioner.TimeBasedAvroWriterPartitioner # writer.partition.pattern=yyyy/MM/dd/HH
    27. 27. Kafka Pusher Job 28 Push changes from Wikipedia to a Kafka topic. 43ab41c0d0d59cb586
    28. 28. Gobblin Metrics and Events 29 Gobblin emits operational metrics and events. metrics.enabled=true metrics.reporting.file.enabled=true metrics.log.dir=/home/gobblin/metrics Write metrics to file metrics.enabled=true metrics.reporting.kafka.enabled=true metrics.reporting.kafka.brokers=localhost:9092 metrics.reporting.kafka.topic.metrics=GobblinMetrics metrics.reporting.kafka.format=avro metrics.reporting.kafka.schemaVersionWriterType=NOOP Write metrics to Kafka
    29. 29. Gobblin Metric Flattening for Pinot 30 gobblin-jobs/gobblin-metrics-flattener.pull job.template=templates/kafka-to-kafka.template job.schedule=0 0/5 * * * ? inputTopics=GobblinMetrics outputTopic=FlatMetrics gobblin.source.kafka.extractorType=AVRO_FIXED_SCHEMA gobblin.source.kafka.fixedSchema.GobblinMetrics=<schema> converter.classes=gobblin.converter.GobblinMetricsFlattenerCo nverter,gobblin.converter.avro.AvroToJsonStringConverter,gobb lin.converter.string.StringToBytesConverter
    30. 30. Distributed Gobblin 31 Hadoop / YARN Azkaban Mode • AzkabanGobblinDaemon (multi-job) • AzkabanJobLauncher (single job) MR mode • bin/ (single job) YARN mode • GobblinYarnAppLauncher (experimental) AWS Set up Gobblin cluster on AWS nodes. In development: Distributed job running for standalone Gobblin
    31. 31. Pinot Demo Outline 32 1. Set up Pinot and create a table 2. Load offline data into the table 3. Query Pinot 4. Configure realtime (streaming) data ingestion
    32. 32. Pinot Setup 33 git clone the latest version mvn -DskipTests install
    33. 33. Pinot Startup 34 cd pinot-distribution/target/pinot-0.016-pkg bin/ -dataDir /data/pinot/controller-data & bin/ & bin/ -dataDir /data/pinot/server-data & After Zookeeper and Kafka started. This will: • Start a controller listening on localhost:9000 • Start a broker listening on localhost:8099 • Start a server, although clients don’t connect to it directly.
    34. 34. Pinot architecture 35
    35. 35. Creating a table 36 bin/ AddTable -filePath flights/flights-definition.json -exec • Tables in Pinot are created using a JSON-based configuration format • This configuration defines several parameters, such as the retention period, time column and for which columns to create inverted indices
    36. 36. 37 { "tableIndexConfig": { "invertedIndexColumns":[], "loadMode":"MMAP”, "lazyLoad":"false” }, "tenants":{"server":"airline","broker":"airline_broker"}, "tableType":"OFFLINE","metadata":{}, "segmentsConfig":{ "retentionTimeValue":"700”, "retentionTimeUnit":"DAYS“, "segmentPushFrequency":"daily“, "replication":1, "timeColumnName":"DaysSinceEpoch”, "timeType":"DAYS”, "segmentPushType":"APPEND”, "schemaName":"airlineStats”, "segmentAssignmentStrategy": "BalanceNumSegmentAssignmentStrategy” }, "tableName":"airlineStats“ }
    37. 37. Loading data into Pinot 38 • Data in Pinot is stored in segments, which are pre- indexed units of data • To load our Avro-formatted data into Pinot, we’ll run a segment conversion (which can either be run locally or on Hadoop) to turn our data into segments • We’ll then upload our segments into Pinot
    38. 38. Converting data into segments 39 • For this demo, we’ll do this locally: • In a production environment, you’ll want to do this on Hadoop: • See for Hadoop configuration bin/ CreateSegment -dataDir flights - outDir converted-segments -tableName flights - segmentName flights hadoop jar pinot-hadoop-0.016.jar SegmentCreation
    39. 39. Uploading segments to Pinot 40 Uploading segments in Pinot is done through a standard HTTP file upload; we also provide a job to do it from Hadoop. Locally: On Hadoop: bin/ UploadSegment -segmentDir converted-segments hadoop jar pinot-hadoop-0.016.jar SegmentTarPush
    40. 40. Querying Pinot 41 • Pinot offers a REST API to send queries, which then return a JSON-formatted query response • There is also a Java client, which provides a JDBC- like API to send queries • For debugging purposes, it’s also possible to send queries to the controller through a web interface, which forwards the query to the appropriate broker
    41. 41. Querying Pinot 42 bin/ PostQuery -query "select count(*) from flights" { "numDocsScanned":844482, "aggregationResults”: [{"function":"count_star","value":"844482"}], "timeUsedMs":16, "segmentStatistics":[], "exceptions":[], "totalDocs":844482 }
    42. 42. Adding realtime ingestion 43 • We could make our data fresher by running an offline push job more often, but there’s a limit as to how often we can do that • In Pinot, there are two types of tables: offline and realtime (eg. streaming from Kafka) • Pinot supports merging offline and realtime tables at runtime
    43. 43. Realtime table and offline table 44
    44. 44. SELECT SUM(foo) rewrite 45
    45. 45. Configuring realtime ingestion 46 • Pinot supports pluggable decoders to interpret messages fetched from Kafka; there is one for JSON and one for Avro • Pinot also requires a schema, which defines which columns to index, their type and purpose (dimension, metric or time column) • Realtime tables require having a time column, so that query splitting can work properly
    46. 46. Configuring realtime ingestion 47 { "schemaName" : "flights", "timeFieldSpec" : { "incomingGranularitySpec" : { "timeType" : "DAYS”, "dataType" : "INT”, "name" : "DaysSinceEpoch" } }, "metricFieldSpecs" : [ { "name" : "Delayed”, "dataType" : "INT”, "singleValueField" : true }, ... ], "dimensionFieldSpecs" : [ { "name": "Year”, "dataType" : "INT”, "singleValueField" : true }, { "name": "DivAirports”, "dataType" : "STRING”, "singleValueField" : false }, ... ], }
    47. 47. Operating in Production 48
    48. 48. Pipeline in production 1. Fault tolerance 2. Performance 3. Retention 4. Metrics 5. Offline and realtime 6. Indexing and sorting 49
    49. 49. Pipeline in production: Fault tolerance Gobblin: • Retry work units on failure • Commit policies for isolating failures. • Require external tool for daemon failures (cron, Azkaban) Pinot: • Supports replication data: fault tolerance and read scaling • By design, no single point of failure; at Linkedin multiple controllers, servers and brokers, any one can fail without impacting availability. 50
    50. 50. Pipeline in production: Performance Gobblin: • Run in distributed mode. • 1 or more tasks per container. Supports bin packing of tasks. • Bottleneck at job driver (fix in progress). Pinot: • Offline clusters can be resized at runtime without service interruption: just add more nodes and rebalance the cluster. • Realtime clusters can also be resized, although new replicas need to reconsume the contents of the Kafka topic (this limitation should be gone in Q4 2016). 51
    51. 51. Pipeline in production: Retention Gobblin: • Data retention job available in Gobblin suite. • Supports common policies (time, newest K) as well as custom policies. Pinot: • Configurable retention feature: data expired and removed automatically without user intervention. • Configurable independently for realtime and offline tables: for example, one might have 90 days of retention for offline data and 7 days of retention for realtime data. 52
    52. 52. Pipeline in production: Metrics Gobblin: • Metrics and events emitted by all jobs to any sink: timings, records processed per stage, etc. • Can add custom instrumentation to pipeline. Pinot: • Emits metrics that can be used to monitor the system to make sure everything is running correctly. • Key metrics: per table query latency and rate, GC rate, and number of available replicas. • For debugging, it’s also possible to drill down into latency metrics for the various phases of the query. 53
    53. 53. Pipeline in production: Offline and real time Gobblin: • Mostly offline job. Can run frequently with small batches. • More real time processing in progress. Pinot: • For hybrid clusters (combined offline and real time), overlap between both parts means fewer production issues: • If Hadoop data push job fails, data is served from the real time part; increasing the retention can be done for extended offline data push job failures. • If real time part has issues, offline data has precedence over real time data, thus ensuring that data can be replaced; only the latest data points will be unavailable. 54
    54. 54. Pipeline in production: Indexing and sorting • Pinot supports per-table indexes; created at load time so there is no performance hit at runtime for re-indexing. • Pinot optimizes queries where data is sorted on at least one of the filter predicates; for example “Who viewed my profile” data is sorted on viewerId. • Pinot supports sorting data ingested from realtime when writing to disk. 55
    55. 55. Conclusions 56 1 Analytics pipeline collecting data from a variety of sources 2 Gobblin provides universal data ingestion and easy extensibility 3 Pinot provides offline and real time analytics querying 4 Easy, flexible setup of analytics pipeline 5 Production considerations around scale, fault tolerance, etc.
    56. 56. Who is using this? 57
    57. 57. Development Teams 58 P not
    58. 58. Find out more: ©2015 LinkedIn Corporation. All Rights Reserved. Find out more: ©2015 LinkedIn Corporation. All Rights Reserved. 59 P not