Cloudera Kudu Introduction

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1 Cloudera Kudu Introduction Zbigniew Baranowski Based on:

2 What is KUDU? New storage engine for structured data (tables) does not use HDFS! Columnar store Mutable (insert, update, delete, scan) Written in C++ Apache-licensed open source Currently in beta version

3 Hadoop and KUDU

4 Yet another engine to store data? HDFS excels at Scanning of large amount of data at speed Accumulating data with high throughput HBASE (on HDFS) excels at Fast random lookups and writing by key Making data mutable

5 KUDU tries to fill the gap

6 Addressing evolution in hardware Hard drives -> solid state disks Random scans in milliseconds Analytics no longer IO bound -> CPU bound RAM is getting cheaper can be used at greater scale

7 Table oriented storage Table has Hive/RDBMS like schema Primary key (one or many columns), NO secondary indexes Finite number of columns Each column has name and type Horizontally partitioned (range, hash) called tablets Tablets typically have 3 or 5 replicas

8 Table access and manipulations Operations on tables (NoSQL) insert, update, delete, scan Java and C++ API Integrated with Impala, MapReduce, Spark more are coming

9 KUDU table with Impala Source: CREATE TABLE `metrics` ( `host` STRING, `metric` STRING, `timestamp` INT, `value` DOUBLE ) TBLPROPERTIES( 'storage_handler' = 'com.cloudera.kudu.hive.kudustoragehandler', 'kudu.table_name' = 'metrics', 'kudu.master_addresses' = 'quickstart.cloudera:7051', 'kudu.key_columns' = 'host, metric, timestamp' ); insert into metrics values ( myhost, temp1, , ); select count(distinct metric) from metrics; delete from metrics where host= myhost and metric= temp1..;

10 KUDU with Spark import org.kududb.mapreduce._ import org.apache.hadoop.conf.configuration import org.kududb.client._ import org.apache.hadoop.io.nullwritable; val conf = new Configuration conf.set("kudu.mapreduce.master.address", "quickstart.cloudera"); conf.set("kudu.mapreduce.input.table", "metrics"); conf.set("kudu.mapreduce.column.projection", "host,metric,timestamp,value"); val kudurdd = sc.newapihadooprdd(conf, classof[kudutableinputformat], classof[nullwritable], classof[rowresult]) // Print the first five values kudurdd.values.map(r => r.rowtostring()).take(5).foreach(x => print(x + "\n"))

11 Data Consistency Reading Snapshot consistency Point in time queries (based on provided timestamp) Writing Single row mutations done atomically across all columns No multi-row transactions

12 Architecture overview Master server (single) Keeps metadata replicated Catalog (tables definitions) in a KUDU table (cached) Coordination (full view of the cluster) Tablets directory (tablets locations) (in memory) Fast failover supported Tablets Server (worker nodes) Stores/servers tablets On local disks (no HDFS) Tracks status of tablets replicas (followers)

13 Tables and tablets Metadata Data

14 When to use? When sequential and random data access is required simultaneously When simplification of a data ingest is needed When updates on data are required Examples Time series Streaming data, immediately available Online reporting

15 Typical low latency ingestion flow

16 Simplified ingestion flow with KUDU

17 Benchmarking by Cloudera

18 Benchmarking by customer (Xiaomi)

19 KUDU under the hood

20 Data replication - Raft consensus Master Client Tablet server X Tablet 1 (leader) WAL Tablet server Y Tablet server Z Tablet 1 (follower) WAL Tablet 1 (follower) WAL

21 Data Insertion (without uniqueness check) MemRowSet DiskRowSet1 (32MB) PK B+tree Row1,Row2,Row3 Flush DiskRowSet2 (32MB) Col1 Col2 Col3 PK {min, max} Bloom filters PK {min, max} Row: Col1,Col2, Col3 Leafs sorted by Primary Key Columnar store encoded similarly to Parquet Rows sorted by PK. Interval tree Bloom filters for PK ranges. Stored in cached btree INSERT Interval tree keeps track of PK ranges within DiskRowSets PK Col1 Col2 Col3 Bloom filters Tablets Server There might be Ks of sets per tablet

22 Btree index Column encoding within DiskRowSet Index for given column For PK: maps row keys to pages Pages with data Values Size 256KB For a standard column: maps row offsets to pages Page metadata Values Page metadata Values Page metadata Pages are encoded witha variety of encodings, such as dictionary encoding, bitshuffle, or front coding Pages can be compressed: LZ4, gzip, or bzip2 Values Page metadata

23 DiskRowSet compaction DiskRowSet1 (32MB) PK {A, G} DiskRowSet2 (32MB) PK {B, E} Compact DiskRowSet1 (32MB) PK {A, D} DiskRowSet2 (32MB) PK {E, G} Periodical task Removes deleted rows Reduces the number of sets with overlapping PK ranges Does not create bigger DiskRowSets 32MB size for each DRS is preserver

24 Data updates PK has to be provided for UPDATE and DELETE MemRowSet B+tree Row1,Row2,Row3 Using DRS PK ranges and boom filters UPDATE Set Col2=x where Col1=y DiskRowSet1 (32MB) PK {min, max} If the row is there, get its PK Col1 offset Col2 Col3 Bloom DiskRowSet2 (32MB) PK filters PK {min, max} Compactions Col1 Col2 Col3 Bloom filters Compactions are done periodically DeltaStore (on disk) Flush MemDeltaStore B+tree (row_offset,time),

25 Summary KUDU is NOT a SQL database, a filesystem, an in-memory database not a direct replacement for Hbase or HDFS KUDU is trying to be a compromise between Fast sequential scans Fast random reads Simplifies data ingestion model

26 Learn more Video: Whitepaper: KUDU project: Get Cloudera Quickstart VM and test it

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