Spark Streaming, Machine Learning, Graph Processing and Approximations with Kinesis
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Spark Streaming Kinesis Machine Learning Graph Processing Lambda Architecture Approximations
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Spark Streaming, Machine Learning, Graph Processing and Approximations with Kinesis
- 1. Spark and Friends: Spark Streaming, Machine Learning, Graph Processing, Lambda Architecture, Approximations Chris Fregly East Bay Java User Group Oct 2014 Kinesis Streaming
- 2. Who am I? Former Netflix’er (netflix.github.io) Spark Contributor (github.com/apache/spark) Founder (fluxcapacitor.com) Author (effectivespark.com, sparkinaction.com)
- 4. Not-so-Quick Poll • Spark, Spark Streaming? • Hadoop, Hive, Pig? • Parquet, Avro, RCFile, ORCFile? • EMR, DynamoDB, Redshift? • Flume, Kafka, Kinesis, Storm? • Lambda Architecture? • PageRank, Collaborative Filtering, Recommendations? • Probabilistic Data Structs, Bloom Filters, HyperLogLog?
- 5. Quick Poll Raiders or 49’ers?
- 6. “Streaming” Kinesis Streaming Video Streaming Piping Big Data Streaming
- 7. Agenda • Spark, Spark Streaming • Use Cases • API and Libraries • Machine Learning • Graph Processing • Execution Model • Fault Tolerance • Cluster Deployment • Monitoring • Scaling and Tuning • Lambda Architecture • Probabilistic Data Structures • Approximations
- 8. Timeline of Spark Evolution
- 9. Spark and Berkeley AMP Lab Berkeley Data Analytics Stack (BDAS)
- 10. Spark Overview • Based on 2007 Microsoft Dryad paper • Written in Scala • Supports Java, Python, SQL, and R • Data fits in memory when possible, but not required • Improved efficiency over MapReduce – 100x in-memory, 2-10x on-disk • Compatible with Hadoop – File formats, SerDes, and UDFs
- 11. Spark Use Cases • Ad hoc, exploratory, interactive analytics • Real-time + Batch Analytics – Lambda Architecture • Real-time Machine Learning • Real-time Graph Processing • Approximate, Time-bound Queries
- 12. Explosion of Specialized Systems
- 13. Unified High-level Spark Libraries • Spark SQL (Data Processing) • Spark Streaming (Streaming) • MLlib (Machine Learning) • GraphX (Graph Processing) • BlinkDB (Approximate Queries) • Statistics (Correlations, Sampling, etc) • Others – Shark (Hive on Spark) – Spork (Pig on Spark)
- 14. Benefits of Unified Libraries • Advancements in higher-level libraries are pushed down into core and vice-versa • Examples – Spark Streaming • GC and memory management improvements – Spark GraphX • IndexedRDD for random, hashed-based access within a partition versus scanning entire partition – Spark Core • Sort-based Shuffle
- 15. Resilient Distributed Dataset (RDD)
- 16. RDD Overview • Core Spark abstraction • Represents partitions across the cluster nodes • Enables parallel processing on data sets • Partitions can be in-memory or on-disk • Immutable, recomputable, fault tolerant • Contains transformation history (“lineage”) for whole data set
- 17. Types of RDDs • Spark Out-of-the-Box – HadoopRDD – FilteredRDD – MappedRDD – PairRDD – ShuffledRDD – UnionRDD – DoubleRDD – JdbcRDD – JsonRDD – SchemaRDD – VertexRDD – EdgeRDD • External – CassandraRDD (DataStax) – GeoRDD (Esri) – EsSpark (ElasticSearch)
- 18. RDD Partitions
- 20. Demo! • Load user data • Load gender data • Join user data with gender data • Analyze lineage
- 21. Join Optimizations • When joining large dataset with small dataset (reference data) • Broadcast small dataset to each node/partition of large dataset (one broadcast per node)
- 22. Spark API
- 23. Spark API Overview • Richer, more expressive than MapReduce • Native support for Java, Scala, Python, SQL, and R (mostly) • Unified API across all libraries • Operations – Transformations (lazy evaluation) – Actions (execute transformations)
- 24. Transformations
- 25. Actions
- 27. Job Scheduling • Job – Contains many stages • Contains many tasks • FIFO – Long-running jobs may starve resources for other jobs • Fair – Round-robin to prevent resource starvation • Fair Scheduler Pools – High-priority pool for important jobs – Separate user pools – FIFO within the pool – Modeled after Hadoop Fair Scheduler
- 28. Spark Execution Model Overview • Parallel, distributed • DAG-based • Lazy evaluation • Allows optimizations – Reduce disk I/O – Reduce shuffle I/O – Single pass through dataset – Parallel execution – Task pipelining • Data locality and rack awareness • Worker node fault tolerance using RDD lineage graphs per partition
- 30. Task Pipelining
- 32. Types of Cluster Deployments • Spark Standalone (default) • YARN – Allows hierarchies of resources – Kerberos integration – Multiple workloads from disparate execution frameworks • Hive, Pig, Spark, MapReduce, Cascading, etc • Mesos – Coarse-grained • Single, long-running Mesos tasks runs Spark mini tasks – Fine-grained • New Mesos task for each Spark task • Higher overhead • Not good for long-running Spark jobs like Spark Streaming
- 35. Master High Availability • Multiple Master Nodes • ZooKeeper maintains current Master • Existing applications and workers will be notified of new Master election • New applications and workers need to explicitly specify current Master • Alternatives (Not recommended) – Local filesystem – NFS Mount
- 36. Spark After Dark (sparkafterdark.com)
- 37. Spark After Dark ~ Tinder
- 38. Mutual Match!
- 39. Goal: Increase Matches (1/2) • Top Influencers – PageRank – Most desirable people • People You May Know – Shortest Path – Facebook-enabled • Recommendations – Alternating Least Squares (ALS)
- 40. Goal: Increase Matches (2/2) • Cluster on Interests, Height, Religion, etc – K-Means – Nearest Neighbor • Textual Analysis of Profile Text – TF/IDF – N-gram – LDA Topic Extraction
- 41. Demo!
- 42. Spark Streaming
- 43. Spark Streaming Overview • Low latency, high throughput, fault-tolerance (mostly) • Long-running Spark application • Supports Flume, Kafka, Twitter, Kinesis, Socket, File, etc. • Graceful shutdown, in-flight message draining • Uses Spark Core, DAG Execution Model, and Fault Tolerance • Submits micro-batch jobs to the cluster
- 45. Spark Streaming Use Cases • ETL and enrichment of streaming data on injest • Operational dashboards • Lambda Architecture
- 46. Discretized Stream (DStream) • Core Spark Streaming abstraction • Micro-batches of RDDs • Operations similar to RDD – operates on underlying RDDs
- 48. Spark Streaming API Overview • Rich, expressive API similar to core • Operations – Transformations (lazy) – Actions (execute transformations) • Window and State Operations • Requires checkpointing to snip long-running DStream lineage • Register DStream as a Spark SQL table for querying?! Wow.
- 50. DStream Actions
- 51. Window and State DStream Operations
- 52. DStream Window and State Example
- 53. Spark Streaming Cluster Deployment
- 54. Spark Streaming Cluster Deployment
- 55. Adding Receivers
- 56. Adding Workers
- 57. Spark Streaming + Kinesis
- 58. Spark Streaming + Kinesis Architecture
- 60. Demo! Kinesis Streaming https://github.com/apache/spark/blob/master/extras/kinesis-asl/src/main/… Scala: …/scala/org/apache/spark/examples/streaming/KinesisWordCountASL.scala Java: …/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java
- 61. Spark Streaming Fault Tolerance
- 62. Characteristics of Sources • Buffered – Flume, Kafka, Kinesis – Allows replay and back pressure • Batched – Flume, Kafka, Kinesis – Improves throughput at expense of duplication on failure • Checkpointed – Kafka, Kinesis – Allows replay from specific checkpoint
- 63. Message Delivery Guarantees • Exactly once [1] – No loss – No redeliver – Perfect delivery – Incurs higher latency for transactional semantics – Spark default per batch using DStream lineage – Degrades to less guarantees depending on source • At least once [1..n] – No loss – Possible redeliver • At most once [0,1] – Possible loss – No redeliver – *Best configuration if some data loss is acceptable • Ordered – Per partition: Kafka, Kinesis – Global across all partitions: Hard to scale
- 64. Types of Checkpoints Spark 1. Spark checkpointing of StreamingContext DStreams and metadata 2. Lineage of state and window DStream operations Kinesis 3. Kinesis Client Library (KCL) checkpoints current position within shard – Checkpoint info is stored in DynamoDB per Kinesis application keyed by shard
- 65. Fault Tolerance • Points of Failure – Receiver – Driver – Worker/Processor • Possible Solutions – Use HDFS File Source for durability – Data Replication – Secondary/Backup Nodes – Checkpoints • Stream, Window, and State info
- 66. Streaming Receiver Failure • Use a backup receiver • Use multiple receivers pulling from multiple shards – Use checkpoint-enabled, sharded streaming source (ie. Kafka and Kinesis) • Data is replicated to 2 nodes immediately upon ingestion – Will spill to disk if doesn’t fit in memory • Possible loss of most-recent batch • Possible at-least once delivery of batch • Use buffered sources for replay – Kafka and Kinesis
- 67. Streaming Driver Failure • Use a backup Driver – Use DStream metadata checkpoint info to recover • Single point of failure – interrupts stream processing • Streaming Driver is a long-running Spark application – Schedules long-running stream receivers • State and Window RDD checkpoints to HDFS to help avoid data loss (mostly)
- 68. Stream Worker/Processor Failure • No problem! • DStream RDD partitions will be recalculated from lineage • Causes blip in processing during node failover
- 69. Spark Streaming Monitoring and Tuning
- 70. Monitoring • Monitor driver, receiver, worker nodes, and streams • Alert upon failure or unusually high latency • Spark Web UI – Streaming tab • Ganglia, CloudWatch • StreamingListener callback
- 71. Spark Web UI
- 72. Tuning • Batch interval – High: reduce overhead of submitting new tasks for each batch – Low: keeps latencies low – Sweet spot: DStream job time (scheduling + processing) is steady and less than batch interval • Checkpoint interval – High: reduce load on checkpoint overhead – Low: reduce amount of data loss on failure – Recommendation: 5-10x sliding window interval • Use DStream.repartition() to increase parallelism of processing DStream jobs across cluster • Use spark.streaming.unpersist=true to let the Streaming Framework figure out when to unpersist • Use CMS GC for consistent processing times
- 74. Lambda Architecture Overview • Batch Layer – Immutable, Batch read, Append-only write – Source of truth – ie. HDFS • Speed Layer – Mutable, Random read/write – Most complex – Recent data only – ie. Cassandra • Serving Layer – Immutable, Random read, Batch write – ie. ElephantDB
- 75. Spark + AWS + Lambda
- 76. Spark + Lambda + GraphX + MLlib
- 77. Demo! • Load JSON • Convert to Parquet file • Save Parquet file to disk • Query Parquet file directly
- 78. Approximations
- 79. Approximation Overview • Required for scaling • Speed up analysis of large datasets • Reduce size of working dataset • Data is messy • Collection of data is messy • Exact isn’t always necessary • “Approximate is the new Exact”
- 80. Some Approximation Methods • Approximate time-bound queries – BlinkDB • Bernouilli and Poisson Sampling – RDD: sample(), takeSample() • HyperLogLog PairRDD: countApproxDistinctByKey() • Count-min Sketch • Spark Streaming and Twitter Algebird • Bloom Filters – Everywhere!
- 81. Approximations In Action Figure: Memory Savings with Approximation Techniques (http://highlyscalable.wordpress.com/2012/05/01/probabilistic-structures-web-analytics-data-mining/)
- 82. Spark Statistics Library • Correlations – Dependence between 2 random variables – Pearson, Spearman • Hypothesis Testing – Measure of statistical significance – Chi-squared test • Stratified Sampling – Sample separately from different sub-populations – Bernoulli and Poisson sampling – With and without replacement • Random data generator – Uniform, standard normal, and Poisson distribution
- 83. Summary • Spark, Spark Streaming Overview • Use Cases • API and Libraries • Machine Learning • Graph Processing • Execution Model • Fault Tolerance • Cluster Deployment • Monitoring • Scaling and Tuning • Lambda Architecture • Probabilistic Data Structures • Approximations http://effectivespark.com http://sparkinaction.com Thanks!! Chris Fregly @cfregly