Introducing Kudu

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Introducing KuduJeremy Beard | Senior Solutions ArchitectDecember 2015


Presenter

• Jeremy Beard• Senior Solutions Architect at Cloudera

• Three years in big data• Six years in data warehousing

• [email protected]


Current storage landscape in Hadoop

HDFS excels at:• Efficiently scanning large amounts

of data• Accumulating data with high

throughputHBase excels at:• Efficiently finding and writing

individual rows• Making data mutable

Gaps exist when these properties are needed simultaneously


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• 3D XPoint memory (1000x faster than NAND, cheaper than RAM)

• RAM is cheaper and more abundant:• 64->128->256GB over last few years

• Takeaway 1: The next bottleneck is CPU, and current storage systems weren’t designed with CPU efficiency in mind• Takeaway 2: Column stores are feasible for random access


KuduStorage for Fast Analytics on Fast Data

• New updating column store for Hadoop• Simplifies the architecture for building analytic

applications on changing data• Designed for fast analytic performance• Natively integrated with Hadoop

• Donated as incubating project at Apache Software Foundation

• Beta now availableSTRUCTURED

SqoopUNSTRUCTURED

Kafka, Flume

PROCESS, ANALYZE, SERVE

UNIFIED SERVICES

RESOURCE MANAGEMENTYARN

SECURITYSentry, RecordService

FILESYSTEMHDFS

RELATIONALKudu

NoSQLHBase

STORE

INTEGRATE

BATCHSpark, Hive, Pig

MapReduce

STREAMSpark

SQLImpala

SEARCHSolr

SDKKite


• High throughput for big scans (columnar storage and replication)Goal: Within 2x of Parquet

• Low-latency for short accesses (primary key indexes and quorum design)Goal: 1ms read/write on SSD

• Database-like semantics (initially single-row ACID)

• Relational data model• SQL query• “NoSQL” style scan/insert/update (Java client)

Kudu design goals


Kudu basic design• Apache-licensed open source software

• Structured data model

• Basic construct: tables• Tables broken down into tablets (roughly equivalent to partitions)

• Architecture supports geographically disparate, active/active systems • Not the initial design goal


What Kudu is not• Not a SQL interface• Just the storage layer• “BYO SQL”

• Not a file system• Data must have tabular structure

• Not an application that runs on HDFS• An alternative, native Hadoop storage engine

• Not a replacement for HDFS or HBase• Select the right storage for the right use case• Cloudera will continue to support and invest in all three


Kudu data model

• Tables have a RDBMS-like schema• Finite number of columns (unlike HBase/Cassandra)• Types: BOOL, INT8/16/32/64, FLOAT, DOUBLE, STRING, BINARY, TIMESTAMP• Some subset of columns makes up a primary key

• Fast random reads/writes by primary key• No secondary indexes (yet)

• Columnar layout on disk• Lazy materialization• Encoding and compression options

9


Table partitioning

• Hash bucketing• Distribute records by hash of partition column(s)• N buckets leads to N tablets

• Range partitioning• Distribute records by ranges of the partition column(s)• N split keys leads to N tablets

• Can be a mix for different columns of the primary key


Consistency model

• Consistency and replication enforced by Raft consensus (similar to Paxos)• Replication by operation not data

• Single-row transactions now• Multi-row transactions later

• Geo-distributed replicas will be possible under strict time synchronization

• Techniques drawn from Google Spanner and others


Kudu interfaces

• NoSQL-style APIs• Insert(), Update(), Delete(), Scan()• Java and C++ now• Python soon

• Integrations with MapReduce, Spark, and Impala

• No direct access to underlying Kudu tablet files

• Beta does not have authentication, authorization, encryption


Impala integration

• Opens up Kudu to JDBC/ODBC clients

• Intuitive way to get data into Kudu• INSERT INTO kudu SELECT * FROM csv;

• Additional commands• UPDATE• DELETE• Efficient INSERT VALUES

• Runs on the Kudu C++ client


Performance characteristics

• Very CPU efficient• Written in modern C++, uses specialized CPU instructions, JIT compilation

• Latency mostly driven by storage hardware capabilities• Expect sub-millisecond response on SSDs and upcoming technologies

• No garbage collection allows very large memory footprint with no pauses

• Bloom filters reduce the need for many disk accesses


Operating Kudu

• Easiest through Cloudera Manager integration• Separate parcel for now

• Kudu is always compacting• No minor vs. major compaction• No compaction latency spikes

• Web UI is full of metrics and logs


Cluster layout

• One or multiple masters• Only one in current beta• Low CPU and memory impact

• One tablet server per worker node• Can share disks with HDFS• One SSD per worker node just for Kudu WAL can speed up writes

• No dependencies on other Hadoop ecosystem components• But interfacing components like Impala or Spark do


Real-time analytics in Hadoop todayMerging in new data = storage complexity

Downsides:

● Multiple storage layers

● Latest data is hidden

● Files are messy

● Complex to do updates without breaking running queriesNew Partition

Most Recent Partition

Historic Data

HBase

Parquet File

Have we accumulated enough data?

Reorganize HBase file

into Parquet

• Wait for running operations to complete • Define new Impala partition referencing

the newly written Parquet file

Incoming Data (Messaging

System)

Reporting Request

HDFS + Impala


Real-time analytics in Hadoop with Kudu

Improvements:

● One system to operate

● No schedules or background processes

● Handle late arrivals or data corrections with ease

● New data available immediately for analytics or operations

Historical and Real-timeData

Incoming Data (Messaging

System)

Reporting Request

Kudu + Impala


Kudu for data warehousing

• Near real time data visibility• BI tools can display events that happened seconds earlier

• Excellent for star schemas• Fast scans of deep fact tables• Efficient wide fact tables• Simplified updates of slowly changing dimensions


Near real time data warehousing on Kudu


Resources

Join the communityhttp://[email protected]

Download the betacloudera.com/downloads

Read the whitepapergetkudu.io/kudu.pdf

http://getkudu.io/

http://www.cloudera.com/downloads

http://getkudu.io/kudu.pdf





Thank [email protected]

Data & Analytics

Introducing Kudu