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![Background: What is in an RDD?
•Dependencies
• Partitions (with optional localityinfo)
• Compute function: Partition =>Iterator[T]
Opaque Computation
& Opaque Data](https://image.slidesharecdn.com/jumpstartwithspark2-161216190918/85/Jump-Start-with-Apache-Spark-2-0-on-Databricks-59-320.jpg)
![Type-safe:operate
on domain objects
with compiled
lambda functions
8
Dataset API in Spark 2.0
val df = spark.read.j son("people.json")
/ / Convert data to domain obj ects.
case cl ass Person(name: Stri ng, age: I n t )
val ds: Dataset[Person] = df.as[Person]
val fi l terD S = d s . f i l t er ( _ . age > 30)
// will return DataFrame=Dataset[Row]
val groupDF = ds.filter(p=>
p.name.startsWith(“M”))
.groupBy(“name”)
.avg(“age”)](https://image.slidesharecdn.com/jumpstartwithspark2-161216190918/85/Jump-Start-with-Apache-Spark-2-0-on-Databricks-61-320.jpg)
![Structured Streaming ModelTrigger: every 1 sec
1 2 3
result
for data
up to 1
Result
Query
Time
data up
to 1
Input data up
to 2
result
for data
up to 2
data up
to 3
result
for data
up to 3
Output
[complete mode] output only new rows
since last trigger
Result: final operated table
updated every triggerinterval
Output: what part of result to write
to data sink after every trigger
Complete output: Write full result table every time
Append output: Write only new rows that got
added to result table since previous batch
*Not all output modes are feasible withall queries](https://image.slidesharecdn.com/jumpstartwithspark2-161216190918/85/Jump-Start-with-Apache-Spark-2-0-on-Databricks-85-320.jpg)
![Streaming ETL with DataFrames
1 2 3
Result
Input
Output
[append mode]
new rows
in result
of 2
new rows
in result
of 3
input = spark.readStream
.format("json")
.load("source-path")
result = input
.select("device", "signal")
.where("signal > 15")
result.writeStream
.format("parquet")
.start("dest-path")](https://image.slidesharecdn.com/jumpstartwithspark2-161216190918/85/Jump-Start-with-Apache-Spark-2-0-on-Databricks-89-320.jpg)
![Watermarking for handling late data
[Spark 2.1]
94
input.withWatermark("event-time", "15 min")
.groupBy(
$"device-type",
window($"event-time", "10 min"))
.avg("signal")
Continuously compute 10
minute average while data
can be 15 minutes late](https://image.slidesharecdn.com/jumpstartwithspark2-161216190918/85/Jump-Start-with-Apache-Spark-2-0-on-Databricks-94-320.jpg)
![Continuous Incremental Execution
100
Planner
Incremental
Execution 2
Offsets:[106-197] Count: 92
Plannerpollsfor
new data from
sources
Incremental
Execution 1
Offsets:[19-105] Count: 87
Incrementally executes
new data and writesto sink](https://image.slidesharecdn.com/jumpstartwithspark2-161216190918/85/Jump-Start-with-Apache-Spark-2-0-on-Databricks-100-320.jpg)
![Continuous Aggregations
Maintain runningaggregate as in-memory state
backed by WAL in file system for fault-tolerance
101
state data generated and used
across incremental executions
Incremental
Execution 1
state:
87
Offsets:[19-105] Running Count: 87
memory
Incremental
Execution 2
state:
179
Offsets:[106-179] Count: 87+92 = 179](https://image.slidesharecdn.com/jumpstartwithspark2-161216190918/85/Jump-Start-with-Apache-Spark-2-0-on-Databricks-101-320.jpg)
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Jump Start with Apache Spark 2.0 on Databricks
This document provides an agenda for a 3+ hour workshop on Apache Spark 2.x on Databricks. It includes introductions to Databricks, Spark fundamentals and architecture, new features in Spark 2.0 like unified APIs, and workshops on DataFrames/Datasets, Spark SQL, and structured streaming concepts. The agenda covers lunch and breaks and is divided into hour and half hour segments.
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Jump Start with Apache Spark 2.0 on Databricks
- 1. Jump Start with Apache®Spark™ 2.x on Databricks Jules S. Damji Spark Community Evangelist Big Data Trunk Meetup, Fremont 12/17/2016 @2twitme
- 2. I have usedApache Spark Before…
- 3. I know thedifference between DataFrame and RDDs…
- 4. $ whoami Spark CommunityEvangelist @ Databricks Developer Advocate @ Hortonworks Software engineering @: Sun Microsystems, Netscape, @Home, VeriSign, Scalix, Centrify, LoudCloud/Opsware, ProQuest https://www.linkedin.com/in/dmatrix @2twitme
- 5. Agenda for thenext 3+ hours • Get to know Databricks • Overview of Spark Fundamentals& Architecture • What’s New in Spark 2.0 • UnifiedAPIs: SparkSessions, SQL, DataFrames, Datasets… • Workshop Notebook1 • Lunch Hour 1.5 • Introduction to DataFrames, DataSets and Spark SQL • Workshop Notebook2 • Break • Introduction to Structured StreamingConcepts • Workshop Notebook3 • Go Home… Hour 1.5
- 6. Get to knowDatabricks • Get Databricks communityedition http://databricks.com/try-databricks
- 7. We are Databricks,the company behind Apache Spark Founded by the creators of Apache Spark in 2013 Share of Spark code contributed by Databricks in 2014 75% 7 Data Value Created Databricks on top of Spark to make big data simple. The Best Place to Run Apache Spark
- 8. MapReduce Generalbatch processing Pregel Dremel Mahout Drill Giraph ImpalaStorm S4 .. . Specialized systems for newworkloads Why Spark? Big Data Systems of Yesterday… Hard to manage, tune, deployHard to combine in pipelines
- 9. MapReduce Generalbatch processing Unified engine Why Spark?Big Data Systems Yesterday.. ? Pregel Dremel Drill Giraph Impala Storm Mahout. . . Specialized systems for newworkloads
- 10. An Analogy Specialized devicesUnified device New applications
- 11. Unified engine acrossdiverse workloads& environments
- 12. Unified engineacross diverseworkloads & environments
- 13.
- 14. A Resilient DistributedDataset (RDD)
- 17. 2 kinds ofActions collect, count, reduce, take, show..saveAsTextFile, (HDFS, S3, SQL, NoSQL, etc.)
- 21. Apache Spark Architecture Deployments Modes •Local • Standalone • YARN • Mesos
- 22. Driver + Executor Driver + Executor Container EC2 Machine Student-1 Notebook Student-2Notebook Container JVM JVM
- 23. 30 GB Container 30GB Container 22 GB JVM 22 GB JVM S S S S S S S S Ex. Ex. 30 GB Container 30 GB Container 22 GB JVM 22 GB JVM S S S S Dr Ex. ... ... Standalone Mode:
- 24. Apache Spark Architecture AnAnatomy ofan Application Spark Application • Jobs • Stages • Tasks
- 25.
- 28. How did weGet Here..? Where we Going..?
- 29. A Brief History 29 2012 Started @ UCBerkeley 2010 2013 Databricks started & donated to ASF 2014 Spark 1.0 & libraries (SQL, ML,GraphX) 2015 DataFrames/Datasets Tungsten ML Pipelines 2016 Apache Spark 2.0 Easier Smarter Faster
- 30. Apache Spark 2.0 •Steps to Bigger& Better Things…. Builds on all we learned in past 2 years
- 33. Major Themes inApache Spark 2.0 TungstenPhase 2 speedupsof 5-10x & Catalyst Optimizer Faster StructuredStreaming real-time engine on SQL / DataFrames Smarter Unifying Datasets and DataFrames & SparkSessions Easier
- 34. Unified API Foundationfor the Future: Spark Sessions, DataFrame Dataset, MLlib, Structured Streaming…
- 35. SparkSession – AUnified entry point to Spark • Conduit to Spark – Creates Datasets/DataFrames – Reads/writes data – Works with metadata – Sets/gets Spark Configuration – Driver uses for Cluster resource management
- 36. SparkSession vs SparkContext SparkSessionsSubsumes • SparkContext • SQLContext • HiveContext • StreamingContext • SparkConf
- 37. SparkSession – AUnified entry point to Spark
- 38.
- 39.
- 40. Long Term • RDDas the low-levelAPI in Spark • For controland certain type-safety inJava/Scala • Datasets & DataFrames give richer semantics& optimizations • For semi-structureddataand DSL like operations • New libraries will increasingly use these as interchange format • Examples: Structured Streaming,MLlib, GraphFrames
- 41. Spark 1.6 vsSpark 2.x
- 42. Spark 1.6 vsSpark 2.x
- 44. Towards SQL 2003 •Today, Spark can run all 99 TPC-DS queries! - New standard compliant parser(with good error messages!) - Subqueries(correlated& uncorrelated) - Approximate aggregatestats - https://databricks.com/blog/2016/07/26/introducing-apache- spark-2-0.html - https://databricks.com/blog/2016/06/17/sql-subqueries-in- apache-spark-2-0.html
- 45. 0 100 200 300 400 500 600 Runtime(seconds) Preliminary TPC-DSSpark2.0 vs 1.6 – Lower is Better Time (1.6) Time (2.0)
- 46. Other notable APIimprovements • DataFrame-based ML pipeline API becomingthe main MLlib API • ML model & pipeline persistence with almost complete coverage • In all programming languages: Scala, Java, Python, R • ImprovedR support • (Parallelizable) User-defined functions in R • Generalized Linear Models (GLMs), Naïve Bayes, Survival Regression, K-Means
- 47. Workshop: Notebook onSparkSession • Import Notebook into your Spark 2.0 Cluster – http://dbricks.co/sswksh1 – http://docs.databricks.com – http://spark.apache.org/docs/latest/api/scala/index.html#org.a pache.spark.sql.SparkSession • Familiarize your self with Databricks Notebook environment • Work through each cell • CNTR +<return> / Shift +Return • Try challenges • Break…
- 48. DataFrames/Datasets & Spark SQL& Catalyst Optimizer
- 49. The not sosecret truth… SQL is not about SQL is about more thanSQL
- 50. Spark SQL: Thewhole story 10 Is About Creating and Running Spark Programs Faster: • Write less code • Read less data • Do less work • optimizerdo the hard work
- 51.
- 52. 52 Using Catalyst inSpark SQL Unresolved Logical Plan Logical Plan Optimized Logical Plan RDDs Selected Physical Plan Analysis Logical Optimization Physical Planning CostModel Physical Plans Code Generation Catalog Analysis: analyzinga logicalplan to resolve references Logical Optimization: logicalplan optimization Physical Planning: Physical planning Code Generation:Compileparts of the query to Java bytecode SQL AST DataFrame Datasets
- 53. Catalyst Optimizations Logical Optimizations CreatePhysical Plan & generate JVM bytecode • Push filter predicates down to data source, so irrelevant data can be skipped • Parquet: skip entire blocks, turn comparisons on strings into cheaper integer comparisons via dictionary encoding • RDBMS: reduce amount of data traffic by pushing predicates down • Catalyst compiles operations into physical plans for execution and generates JVM bytecode • Intelligently choose betweenbroadcast joins and shuffle joins to reduce network traffic • Lower level optimizations: eliminate expensive object allocations and reduce virtual function calls
- 54. # Load partitionedHive table def add_demographics(events): u = sqlCtx.table(" users") events . jo in ( u , events.user_id == u.user_id) .withColumn("c ity" , zipToCity( df .z ip)) # Join on user_id # Run udf to add c it y column PhysicalPlan with Predicate Pushdown and Column Pruning join optimized scan (events) optimized scan (users) events = add_demographics(sqlCtx.load("/data/events", "parquet")) training_data = events.where(events.city == "New York").select(even ts. times ta mp) .co llec t() LogicalPlan filter join PhysicalPlan join scan (users)events file userstable 54 scan (events) filter
- 55. Columns: Predicate pushdown spark.read .format("jdbc") .option("url","jdbc:postgresql:dbserver") .option("dbtable", "people") .load() .where($"name" === "michael") 55 You Write Spark Translates For Postgres SELECT * FROM people WHERE name = 'michael'
- 56. 43 Spark Core (RDD) Catalyst DataFrame/DatasetSQL MLPipelines Structured Streaming {JSON } JDBC andmore… FoundationalSpark2.0 Components Spark SQL GraphFrames
- 57.
- 58.
- 59. Background: What isin an RDD? •Dependencies • Partitions (with optional localityinfo) • Compute function: Partition =>Iterator[T] Opaque Computation & Opaque Data
- 60. Structured APIs InSpark 60 SQL DataFrames Datasets Syntax Errors Analysis Errors Runtime Compile Time Runtime Compile Time Compile Time Runtime Analysis errors are reported before a distributed job starts
- 61. Type-safe:operate on domain objects withcompiled lambda functions 8 Dataset API in Spark 2.0 val df = spark.read.j son("people.json") / / Convert data to domain obj ects. case cl ass Person(name: Stri ng, age: I n t ) val ds: Dataset[Person] = df.as[Person] val fi l terD S = d s . f i l t er ( _ . age > 30) // will return DataFrame=Dataset[Row] val groupDF = ds.filter(p=> p.name.startsWith(“M”)) .groupBy(“name”) .avg(“age”)
- 62.
- 63.
- 64. Project Tungsten • Substantiallyspeed up execution by optimizing CPU efficiency, via: SPARK-12795 (1) Runtime code generation (2) Exploiting cachelocality (3) Off-heap memory management
- 65. 6 “bricks” Tungsten’s CompactRow Format 0x0 123 32L 48L 4 “data” (123, “data”, “bricks”) Nullbitmap Offset to data Offset to data Fieldlengths 20
- 66. Encoders 6 “bricks”0x0 12332L 48L 4 “data” JVM Object Internal Representation MyClass(123, “data”, “ br i c ks”) Encoders translate between domain objects and Spark's internal format
- 67. Datasets: Lightning-fast Serializationwith Encoders
- 68. Performance of CorePrimitives cost per row (single thread) primitive Spark 1.6 Spark 2.0 filter 15 ns 1.1 ns sum w/o group 14 ns 0.9 ns sum w/ group 79 ns 10.7 ns hash join 115 ns 4.0 ns sort (8 bit entropy) 620 ns 5.3 ns sort (64 bit entropy) 620 ns 40 ns sort-merge join 750 ns 700 ns Intel Haswell i7 4960HQ 2.6GHz, HotSpot 1.8.0_60-b27, Mac OS X 10.11
- 70. Workshop: Notebook on DataFrames/Datasets& Spark SQL • Import Notebook into your Spark 2.0 Cluster – http://dbricks.co/sswksh2A – http://dbricks.co/sswksh2 – https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.sql. Dataset • Workthrough each Notebookcell • Try challenges • Break..
- 71.
- 72. Streaming in ApacheSpark Streaming demands newtypes of streaming requirements… 3 SQL MLlib Spark Core GraphX Functional, conciseand expressive Fault-tolerant statemanagement Unified stack with batch processing More than 51%users say most important partof Apache Spark Spark Streaming in production jumped to 22%from 14% Streaming
- 73. Streaming apps are growingmore complex 4
- 74. Streaming computations don’t runin isolation • Need to interact with batch data, interactive analysis, machine learning, etc.
- 75. Use case: IoTDevice Monitoring IoT events from Kafka ETL into long term storage - Preventdata loss - PreventduplicatesStatus monitoring - Handlelate data - Aggregateon windows on eventtime Interactively debug issues - consistency event stream Anomaly detection - Learn modelsoffline - Use online+continuous learning
- 76. Use case: IoTDevice Monitoring Anomalydetection - Learn modelsoffline - Useonline + continuous learning IoT events event stream fromKafka ETL into long term storage - Prevent dataloss Status monitoring - Preventduplicates Interactively - Handle late data debugissues - Aggregateon windows -consistency on eventtime Continuous Applications Not just streaming any more
- 78. The simplest wayto perform streaming analytics is not having to reason about streaming at all
- 79. Static, bounded table Stream asa unbound DataFrame Streaming, unbounded table Single API !
- 80. Stream as unboundedDataFrame
- 81. Gist of StructuredStreaming High-level streaming API built on SparkSQL engine Runs the same computation as batch queriesin Datasets / DataFrames Eventtime, windowing,sessions,sources& sinks Guaranteesan end-to-end exactlyonce semantics Unifies streaming, interactive and batch queries Aggregate data in a stream, then serve using JDBC Add, remove,change queriesat runtime Build and apply ML modelsto your Stream
- 82. Advantages over DStreams 1.Processingwith event-time,dealingwith late data 2. Exactly same API for batch,streaming,and interactive 3. End-to-endexactly-once guaranteesfromthe system 4. Performance through SQL optimizations - Logical plan optimizations, Tungsten, Codegen, etc. - Faster state management for stateful stream processing 82
- 83. Structured Streaming ModelTrigger:every 1 sec 1 2 3 Time data up to 1 Input data up to 2 data up to 3 Query Input: data from source as an append-only table Trigger: newrows appended to table Query: operations on input usual map/filter/reduce newwindow, session ops
- 84. Structured Streaming ModelTrigger:every 1 sec 1 2 3 output for data up to 1 Result Query Time data up to 1 Input data up to 2 output for data up to 2 data up to 3 output for data up to 3 Result: final operated table updated every triggerinterval Output: what part of result to write to data sink after every trigger Complete output: Write full result table every time Output complete output
- 85. Structured Streaming ModelTrigger:every 1 sec 1 2 3 result for data up to 1 Result Query Time data up to 1 Input data up to 2 result for data up to 2 data up to 3 result for data up to 3 Output [complete mode] output only new rows since last trigger Result: final operated table updated every triggerinterval Output: what part of result to write to data sink after every trigger Complete output: Write full result table every time Append output: Write only new rows that got added to result table since previous batch *Not all output modes are feasible withall queries
- 86.
- 87. Batch ETL withDataFrame inputDF = spark.read .format("json") .load("source-path") resultDF = input .select("device", "signal") .where("signal > 15") resultDF.write .format("parquet") .save("dest-path") Read from JSON file Select some devices Write to parquet file
- 88. Streaming ETL withDataFrame input = ctxt.read .format("json") .stream("source-path") result = input .select("device", "signal") .where("signal > 15") result.write .format("parquet") .outputMode("append") .startStream("dest-path") read…stream() creates a streaming DataFrame, doesnot start any of the computation write…startStream() defineswhere & how to outputthe data and starts the processing
- 89. Streaming ETL withDataFrames 1 2 3 Result Input Output [append mode] new rows in result of 2 new rows in result of 3 input = spark.readStream .format("json") .load("source-path") result = input .select("device", "signal") .where("signal > 15") result.writeStream .format("parquet") .start("dest-path")
- 90. Continuous Aggregations Continuously computeaverage signal of each type of device 90 input.groupBy("device-type") .avg("signal")
- 91. Continuous Windowed Aggregations 91 input.groupBy( $"device-type", window($"event-time","10 min")) .avg("signal") Continuously compute average signal of each type of device in last 10 minutes using event-time column
- 92. Joining streams withstatic data kafkaDataset = spark.readStream .format("kafka") .option("subscribe", "iot-updates") .option("kafka.bootstrap.servers", ...) .load() staticDataset = ctxt.read .jdbc("jdbc://", "iot-device-info") joinedDataset = kafkaDataset.join(staticDataset, "device-type") 92 Join streaming data from Kafka with static data via JDBC to enrich the streaming data … … withouthaving to thinkthat you are joining streaming data
- 93. Query Management query =result.write .format("parquet") .outputMode("append") .startStream("dest-path") query.stop() query.awaitTermination() query.exception() query.sourceStatuses() query.sinkStatus() 93 query: a handle to the running streaming computation for managingit - Stop it, wait for it to terminate - Get status - Get error, if terminated Multiple queries can be active at the same time Each query has unique name for keepingtrack
- 94. Watermarking for handlinglate data [Spark 2.1] 94 input.withWatermark("event-time", "15 min") .groupBy( $"device-type", window($"event-time", "10 min")) .avg("signal") Continuously compute 10 minute average while data can be 15 minutes late
- 95.
- 96. Logically: Dataset operations ontable (i.e. as easy to understand asbatch) Physically: Spark automatically runs the queryin streaming fashion (i.e. incrementally andcontinuously) DataFrame LogicalPlan Catalystoptimizer Continuous, incrementalexecution Query Execution
- 97. Batch/Streaming Execution onSpark SQL 97 DataFrame/ Dataset Logical Plan Planner SQL AST DataFrame Unresolved Logical Plan Logical Plan Optimized Logical Plan RDDs Selected Physical Plan Analysis Logical Optimization Physical Planning CostModel Physical Plans Code Generation CatalogDataset Helluvalotofmagic!
- 98. Batch Execution onSpark SQL 98 DataFrame/ Dataset Logical Plan Execution PlanPlanner Run super-optimized Spark jobsto compute results Bytecode generation JVM intrinsics, vectorization Operations on serialized data Code Optimizations MemoryOptimizations Compact and fastencoding Offheap memory Project Tungsten -Phase 1 and 2
- 99. Continuous Incremental Execution Plannerknows how to convert streaming logical plans to a continuous series of incremental execution plans 99 DataFrame/ Dataset Logical Plan Incremental Execution Plan 1 Incremental Execution Plan 2 Incremental Execution Plan 3 Planner Incremental Execution Plan 4
- 100. Continuous Incremental Execution 100 Planner Incremental Execution2 Offsets:[106-197] Count: 92 Plannerpollsfor new data from sources Incremental Execution 1 Offsets:[19-105] Count: 87 Incrementally executes new data and writesto sink
- 101. Continuous Aggregations Maintain runningaggregateas in-memory state backed by WAL in file system for fault-tolerance 101 state data generated and used across incremental executions Incremental Execution 1 state: 87 Offsets:[19-105] Running Count: 87 memory Incremental Execution 2 state: 179 Offsets:[106-179] Count: 87+92 = 179
- 102. Structured Streaming: Recap •High-level streaming API built on Datasets/DataFrames • Eventtime, windowing,sessions,sources& sinks End-to-end exactly once semantics • Unifies streaming, interactive and batch queries • Aggregate data in a stream, then serveusing JDBC Add, remove,change queriesat runtime • Build and applyML models
- 103. Current Status: Spark2.0.2 Basic infrastructureand API - Eventtime, windows,aggregations - Append and Complete output modes - Support for a subsetof batch queries Sourceand sink - Sources: Files,Kafka, Flume,Kinesis - Sinks: Files,in-memory table, unmanaged sinks(foreach) Experimental releaseto set the future direction Not ready for production but good for experiment
- 104. Coming Soon (Dec2016): Spark 2.1.0 Event-time watermarks and old state evictions Status monitoring and metrics - Current status (processing,waiting fordata, etc.) - Current metrics (inputrates, processing rates, state data size, etc.) - Codahale / Dropwizard metrics (reporting to Ganglia,Graphite, etc.) Many stability & performance improvements 104
- 105. Future Direction Stability, stability,stability Supportfor more queries Sessionization More outputmodes Supportfor more sinks ML integrations Make Structured Streaming readyfor production workloads by Spark 2.2/2.3
- 107. Blogs for DeeperTechnical Dive Blog1: https://databricks.com/blog/2016/07/28/ continuous-applications-evolving- streaming-in-apache-spark-2-0.html Blog2: https://databricks.com/blog/2016/07/28/ structured-streaming-in-apache- spark.html
- 108. Resources • Getting StartedGuide w ApacheSpark on Databricks • docs.databricks.com • Spark Programming Guide • StructuredStreaming Programming Guide • Databricks EngineeringBlogs • sparkhub.databricks.com • spark-packages.org
- 109.
- 111. Do you haveany questions for my prepared answers?
- 112. Demo & Workshop:Structured Streaming • Import Notebook into your Spark 2.0 Cluster • http://dbricks.co/sswksh3 (Demo) • http://dbricks.co/sswksh4 (Workshop) • Done!
- 113. 1. Processing withevent-time, dealing with late data - DStream API exposes batch time, hard to incorporate event-time 2. Interoperatestreaming with batch AND interactive - RDD/DStream hassimilar API, butstill requirestranslation 3. Reasoning about end-to-end guarantees - Requirescarefully constructing sinks that handle failurescorrectly - Data consistency in the storage while being updated Pain points with DStreams
- 114. Output Modes Defines whatis written every time there is a trigger Different output modes make sense for differentqueries 22 i n p u t.select ("dev ic e", "s i g n al ") .w r i te .outputMode("append") .fo r m a t( "parq uet") .startStrea m( "de st-pa th ") Append modewith non-aggregationqueries i n p u t.agg( cou nt("* ") ) .w r i te .outputMode("complete") .fo r m a t( "parq uet") .startStrea m( "de st-pa th ") Complete mode with aggregationqueries