Introduction to Apache Kudu
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Philadelphia Hadoop Meetup Talk - April 26th 2017
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Introduction to Apache Kudu
- 2. 2 © Cloudera, Inc. All rights reserved. Apache Kudu Storage for fast (low latency) analy=cs on fast (high throughput) data • Simplifies the architecture for building analy=c applica=ons on changing data • Op=mized for fast analy=c performance • Na=vely integrated with the Hadoop ecosystem of components FILESYSTEM HDFS NoSQL HBASE INGEST – SQOOP, FLUME, KAFKA DATA INTEGRATION & STORAGE SECURITY – SENTRY RESOURCE MANAGEMENT – YARN UNIFIED DATA SERVICES BATCH STREAM SQL SEARCH MODEL ONLINE DATA ENGINEERING DATA DISCOVERY & ANALYTICS DATA APPS SPARK, HIVE, PIG SPARK IMPALA SOLR SPARK HBASE COLUMNAR STORE KUDU
- 4. 4 © Cloudera, Inc. All rights reserved. Previous Hadoop storage landscape HDFS (GFS) excels at: • Batch ingest only (eg hourly) • Efficiently scanning large amounts of data (analy=cs) HBase (BigTable) excels at: • Efficiently finding and wri=ng individual rows • Making data mutable Gaps exist when these proper=es are needed simultaneously
- 5. 5 © Cloudera, Inc. All rights reserved. • High throughput for big scans Goal: Within 2x of Parquet • Low-latency for short accesses Goal: 1ms read/write on SSD • Database-like seman=cs Ini=ally, single-row atomicity • Rela=onal data model • SQL queries should be natural and easy • Include NoSQL-style scan, insert, and update APIs Kudu design goals
- 6. 6 © Cloudera, Inc. All rights reserved. Changing hardware landscape • Spinning disk -> solid state storage • NAND Flash: Up to 450k read 250k write IOPS, about 2GB/sec read and 1.5GB/ sec write throughput, at a price of less than $3/GB and dropping • Intel Optane/3D XPoint memory (1000x faster than Flash, cheaper than RAM) • RAM is cheaper and more abundant: • 64->128->256GB over last few years • Takeaway: The next performance bomleneck is CPU, and current storage systems weren’t designed with CPU efficiency in mind
- 7. 7 © Cloudera, Inc. All rights reserved. Apache Kudu: Scalable and fast structured storage Scalable • Tested up to 400+ nodes (~3PB cluster) • Designed to scale to 1000s of nodes and tens of PBs Fast • Millions of read/write opera=ons per second across cluster • Mul=ple GB/second read throughput per node Tables • Represents data in structured tables like a normal database • Individual record-level access to 100+ billion row tables
- 8. 8 © Cloudera, Inc. All rights reserved. Storing records in Kudu tables • A Kudu table has a SQL-like schema • And a finite number of columns (unlike HBase/Cassandra) • Types: BOOL, INT8, INT16, INT32, INT64, FLOAT, DOUBLE, STRING, BINARY, TIMESTAMP • Some subset of columns makes up a possibly-composite primary key • Fast ALTER TABLE • Java, Python, and C++ NoSQL-style APIs • Insert(), Update(), Delete(), Scan() • SQL via integra=ons with Impala and Spark • Community work in progress / experimental: Drill, Hive
- 11. 11 © Cloudera, Inc. All rights reserved. “Tradi=onal” real-=me analy=cs in Hadoop Fraud detec=on in the real world = storage complexity Considera=ons: • How do I handle failure during this process? • How oyen do I reorganize data streaming in into a format appropriate for repor=ng? • When repor=ng, how do I see data that has not yet been reorganized? • How do I ensure that important jobs aren’t interrupted by maintenance? New Par==on Most Recent Par==on Historical Data HBase Parquet File Have we accumulated enough data? Reorganize HBase file into Parquet • Wait for running opera=ons to complete • Define new Impala par==on referencing the newly wrimen Parquet file Ka{a Repor=ng Request Storage in HDFS
- 12. 12 © Cloudera, Inc. All rights reserved. Real-=me analy=cs in Hadoop with Kudu Improvements: • One system to operate • No cron jobs or background processes • Handle late arrivals or data correc=ons with ease • New data available immediately for analy=cs or opera=ons Historical and Real-=me Data Incoming data (e.g. Ka{a) Repor=ng Request Storage in Kudu
- 19. 19 © Cloudera, Inc. All rights reserved. Tables, tablets, tablet servers and masters • Each table is horizontally par==oned into tablets • Range or hash par==oning • PRIMARY KEY (host, metric, timestamp) DISTRIBUTE BY HASH(timestamp) INTO 100 BUCKETS • Each tablet has N replicas (3 or 5) with Ray consensus • Automa=c fault tolerance • MTTR: ~5 seconds • Tablet servers host tablets on local disk drives • Master services metadata opera=ons • Create/drop tables and tablets • Locate tablets
- 23. 23 © Cloudera, Inc. All rights reserved. Columnar compression {1442825158, 1442826100, 1442827994, 1442828527} Created_at Created_at Diff(created_at) 1442825158 n/a 1442826100 942 1442827994 1894 1442828527 533 64 bits each 11 bits each • Many columns can compress to a few bits per row! • Especially: • Timestamps • Time series values • Low-cardinality strings • Massive space savings and throughput increase!
- 25. 25 © Cloudera, Inc. All rights reserved. What is =me series? Data that can be usefully par==oned and queried based on =me Examples: • Web user ac=vity data (view and click data, tweets, likes) • Machine metrics (CPU u=liza=on, free memory, requests/sec) • Pa=ent data (blood pressure readings, weight changes over =me) • Financial data (stock transac=ons, price fluctua=ons)
- 26. 26 © Cloudera, Inc. All rights reserved. Kudu & =me series data Real =me data inges=on + fast scans = Ideal pla…orm for storing and querying =me series data • Support for many column encodings and compression schemes • Encodings: Plain, dic=onary, bitshuffle, Run Length, Prefix • Compression: LZ4, gzip, bzip2 • Kudu supports a flexible range of par==oning schemes • Par==on by =me range, hash, or both • Parallelizable scans • Scale-out storage system
- 32. 32 © Cloudera, Inc. All rights reserved. Impala integra=on • CREATE TABLE … DISTRIBUTE BY HASH(col1) INTO 16 BUCKETS AS SELECT … FROM … • INSERT / UPDATE / DELETE • Optimizations: predicate pushdown, scan locality, scan parallelism • More optimizations on the way • Not an Impala user? Community working on other integrations (Hive, Drill, Presto, etc)
- 33. 33 © Cloudera, Inc. All rights reserved. Spark DataSource integra=on // Import kudu datasource import org.kududb.spark.kudu._ val kuduDataFrame = sqlContext.read.options( Map("kudu.master" -> "master.address.example.com", "kudu.table" -> "my_table_name")).kudu // Then query using spark api or register a temporary table and use spark sql kuduDataFrame.select("id").filter("id" >= 5).show() // (prints the selection to the console) // Register kuduDataFrame as a temporary table for spark-sql kuduDataFrame.registerTempTable("kudu_table") // Select from the dataframe sqlContext.sql("select id from kudu_table where id >= 5").show() // (prints the sql results to the console)
- 37. 37 © Cloudera, Inc. All rights reserved. TPC-H (analy=cs benchmark) • 75 server cluster • 12 (spinning) disks each, enough RAM to fit dataset • TPC-H Scale Factor 100 (100GB) • Example SQL query (via Impala): • SELECT n_name, sum(l_extendedprice * (1 - l_discount)) as revenue FROM customer, orders, lineitem, supplier, nation, region WHERE c_custkey = o_custkey AND l_orderkey = o_orderkey AND l_suppkey = s_suppkey AND c_nationkey = s_nationkey AND s_nationkey = n_nationkey AND n_regionkey = r_regionkey AND r_name = 'ASIA' AND o_orderdate >= date '1994-01-01' AND o_orderdate < '1995-01-01’ GROUP BY n_name ORDER BY revenue desc;
- 40. 40 © Cloudera, Inc. All rights reserved. What about NoSQL-style random access? (YCSB) • YCSB 0.5.0-snapshot • 10 node cluster (9 workers, 1 master) • 100M row data set • 10M opera=ons each workload
- 42. 42 © Cloudera, Inc. All rights reserved. Geˆng started as a user • On the web: kudu.apache.org • User mailing list: user@kudu.apache.org • Slack chat channel (see web site) • Quickstart VM • Easiest way to get started • Impala and Kudu in an easy-to-install VM • CSD and Parcels • For installa=on on a Cloudera Manager-managed cluster
- 43. 43 © Cloudera, Inc. All rights reserved. Geˆng started as a developer • Source code: github.com/apache/kudu • All commits go here first • Code reviews: gerrit.cloudera.org • All code reviews are public • Public JIRA: issues.apache.org/jira/browse/KUDU • Includes bugs going back to 2013 • Developer mailing list: dev@kudu.apache.org • Apache 2.0 license open source and an ASF project • Contribu=ons welcome and encouraged!
- 44. 44 © Cloudera, Inc. All rights reserved. Project status • First open source beta released in September 2015. • Kudu 1.0.0 version released in September 2016. • Kudu 1.3.1 version was released last week. • Kerberos authen=ca=on, TLS encryp=on, and coarse-grained (cluster-level) authoriza=on • Many Produc=on customers • Users tes=ng up to 400+ nodes so far. • Kudu is a top-level project (TLP) at the Apache Soyware Founda=on • Community-driven open source process.