Cassandra 3.0
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Transcript of Cassandra 3.0
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Cassandra User Group Cologne19:00 - Reception
19:20 - Cassandra libraries for Java developersDuyHai Doan, Cassandra Evangelist at DataStax
20:20 - Apache Cassandra 3.0 Robert Stupp, Committer to Apache Cassandra, CIO contentteam AG
21:00 - Finish & Networking
WELCOME !
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APACHE CASSANDRA 3.0
CASSANDRA USER GROUP COLOGNE
24.03.2015
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Robert Stupp
• CIO contentteam
• Committer to Apache Cassandra
• Coding experience since 1985
• Internet and related technologies since 1992
• @snazy
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&
contentteam is a DataStax Solutions Partner
contentteam is active in Apache Cassandra community
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AGENDA
1.Cassandra history (short) 2.Cassandra 3.0 3.Cassandra community 4.Apache Cassandra vs. DataStax Enterprise
5.One more thing :)
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APACHE CASSANDRA HISTORY
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CASSANDRA HISTORY
• Initially developed at Facebook tobuild a ”continuously available” database • Replication • Globally distributed • Masterless architecture
• Influenced by BigTable and Dynamo
• Today:Huge amount of working installation -few nodes up to 1000+ globally distributed nodes
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CASSANDRA HISTORY
• Open sourced in 2008 • Version 0.3 - July 2009 • Version 0.6 - June 2010 • Version 0.7 - 2011 • Version 1.0 - October 2011 - introduction of DSE • Version 1.2 - December 2012 • Version 2.0 - August 2013 • Version 2.1 - September 2014
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CASSANDRA LIVE RELEASES
• Version 2.0 linecritical bugfixes applied to 2.0 release
• Version 2.1 linebugfixes applied to 2.0 releasesome new, non-intrusive features
• Version 3.0 lots of new featureslots of improvements
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APACHE CASSANDRA 3.0
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CASSANDRA 3.0
DISCLAIMER
• Apache Cassandra 3.0 is still in development • Features might be changed / revoked until 3.0 release
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CASSANDRA 3.0
RELEASE DATE
• Apache Cassandra 3.0 will be releasedwhen it is finished
• Don’t ask for a release date - we don’t know it yet ;)
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CASSANDRA 3.0FEATURES
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CASSANDRA 3.0 NEW FEATURES SUMMARY
• JSON support • User-Defined-Functions + User-Defined-Aggregates • Role based access control • New row cache • Lots of (small) performance improvements
summing up to a huge improvement
• Altogether approx >100 tickets for 3.0 • Plus changes merged from 2.0 via 2.1 to 3.0
and 2.1 to 3.0
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CASSANDRA 3.0 REMOVED FEATURES
• cassandra-cli is removed (as announced)
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CASSANDRA 3.0JSON Support
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JSON SUPPORT
• Allows you to do INSERT and SELECT data using JSON data format
• Format your data to insert using JSON - can save a step to transform data
• Ease web application development - less transformation to/from Cassandra to the browser
• Also nice when using NodeJS
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JSON SUPPORT - EXAMPLE
CREATE TYPE address ( street text, city text, zip_code int, phones set<text>);
CREATE TABLE users ( id uuid PRIMARY KEY, name text, addresses frozen<map<text, address>>);
INSERT INTO users JSON '{"id": "4b856557-7153", "name": "snazy", "addresses": {"work": {"street": "Im Mediapark 6", "city": "Köln", "zip_code": 50670, "phones": ["+492214546200"]}}}';
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this is JSON
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JSON SUPPORT
cqlsh> SELECT JSON * FROM users;
[json]-----------------------------------------------------------{"id": "4b856557-7153", "name": "snazy", "addresses": {"work": {"street": "Im Mediapark 6", "city": "Köln", "zip_code": 50670, "phones": ["+492214546200"]}}}
(1 rows)
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this is JSON
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JSON SUPPORT
• JSON support does not introduce schema-free tables!!
• http://rustyrazorblade.com/2014/07/the-myth-of-schema-less/
• https://blog.compose.io/schema-less-is-usually-a-lie/
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CASSANDRA 3.0
User-Defined-Functions (UDFs) User-Defined-Aggregates (UDAs)
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UDF
• UDF means User Defined Function
• You write the code that’s executed on Cassandra nodes
• Functions are distributed transparently to the whole cluster
• You may not have to wait for a new release for new functionality :)
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UDF CHARACTERISTICS
• „Pure“
• just input parameters
• no state, side effects, dependencies to other code, etc
• Usually deterministic
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UDF 25
Consider a Java function like…import nothing;
public final class MyClass { public static int myFunction ( int argument ) { return argument * 42; } }
This would be your UDF
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UDF EXAMPLE 26
CREATE FUNCTION sinPlusFoo( valueA double, valueB double)RETURNS doubleLANGUAGE javaAS ’return Math.sin(valueA) + valueB;’;
arguments
return type
UDF language
Java codeJava works out of the box!
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UDF/UDA 27
CREATE FUNCTION sin ( value double )RETURNS doubleLANGUAGE javascriptAS ’Math.sin(value);’;
JavaScript works, too
JavaScript code
JavaScript works out of the box!
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JSR 223
• “Scripting for the Java Platform“
• UDFs can be written in Java and JavaScript
• Optionally: Groovy, JRuby, Jython, Scala
• Not: Clojure (JSR 223 implementation’s wrong)
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BEHIND THE SCENES
• Builds Java (or script) source
• Compiles that code (Java class, or compiled script)
• Loads the compiled code
• Migrates the function to all other nodes
• Done - UDF is executable on any node
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TYPES FOR UDFS
• Support for all Cassandra types for arguments and return value
• All means
• Primitives (boolean, int, double, uuid, etc)
• Collections (list, set, map)
• Tuple types, User Defined Types
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UDF/UDA
UDF - For what?
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UDF INVOCATION 32
SELECT sumThat ( colA, colB ) FROM myTable WHERE key = ...
SELECT sin ( foo ) FROM myCircle WHERE pk = ...
Now your application can sum two values in one row - or create the sin of a value!
GREAT NEW FEATURES! Okay - not really…
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UDFS ARE GOOD FOR…
• UDFs on their own are just „nice to have“
• Nothing you couldn’t do better in your application
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User Defined Aggregates !
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USER DEFINED AGGREGATES
Use UDFs to code your own aggregation functions
(Aggregates are things like SUM, AVG, MIN, MAX, etc) Aggregates :
consume values from multiple rows & produce a single result
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UDA EXAMPLE 36
CREATE AGGREGATE minimum ( int ) STYPE int SFUNC minimumState;
arguments
state type
name of the state UDF
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HOW AGGREGATES WORKS
SELECT minimum ( val ) FROM foo
1. Initial state is set to null
2. for each row the state function is called withcurrent state and column value - returns new state
3. After all rows the aggregate returns the last state
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MORE SOPHISTICATED 38
CREATE AGGREGATE average ( int ) SFUNC averageState STYPE tuple<long,int> FINALFUNC averageFinal INITCOND (0, 0);
name of the final UDFinitial state
value
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HOW THAT WORKS
SELECT average ( val ) FROM foo …
1. Initial state is set to (0,0)
2. for each row the state function is called withcurrent state + column value - returns new state
3. After all rows the final function is called with last state
4. final function calculates the aggregate
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UDF/UDA
Now everybody can execute evil code on your
cluster :)
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PERMISSIONS ON UDFS
• There will be permissions to restrict (allow)
• UDF creation (DDL)
• UDF execution (DML)
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SOME FINAL WORDS…
Keep in mind:
• JSR-223 has overhead - Java UDFs are much faster
• Do not allow everyone to create UDFs (in production)
• Keep your UDFs “pure“
• Test your UDFs and user defined aggregates thoroughly
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MORE FINAL WORDS…
• UDFs and user defined aggregates are executed on the coordinator node
• Prefer to use Java-UDFs for performance reasons
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DREAMS
UDFs could be useful for…
• Functional indexes
• Partial indexes
• Filtering
• Distributed GROUP BY
• etc etc
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NOT IN C* 3.0 !
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CASSANDRA 3.0Role based access control
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ROLE BASED ACCESS CONTROL
• Grant/revoke permissions to/from roles • Grant roles to users
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ROLE BASED ACCESS CONTROL
GRANT <permission> ON <resource> TO [[USER] <username> | ROLE <rolename>]
REVOKE <permission> ON <resource> FROM [[USER] <username> | ROLE <rolename>]
LIST <permissionOrAll> [ON <resource>] [OF [[USER] <username> | ROLE <rolename>] [NORECURSIVE]
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MORE NEW AUTHENTICATION/AUTHORIZATION FEATURES
• Authentication/authorization has been reworked for Cassandra 3.0
• Much more options and possibilities • See CASSANDRA-8394 for more information
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CASSANDRA 3.0New row cache
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MOTIVATION NEW ROW CACHE
• Old row cache recommendation:"don't use it"
• Old row cache had data in off-heap,but management data in Java heap
• Resulted in a lot of additional GC pressure
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NEW ROW CACHE
• All data (whole concurrent hash map) is off-heap • Uses APL2 licensed https://github.com/snazy/ohc • Works with really big row cache • Works on really big machines
• But don’t expect a huge performance improvement • Serialization of data to/from off-heap is still a bottleneck • Will work on that bottleneck in future versions
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CASSANDRA 3.0More improvements
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INTERNAL MODERNIZATION IN CASSANDRA 3.0
• Refactor and modernize storage engine (CASSANDRA-8099)
• Modernize schema tables (CASSANDRA-6717)
• CQL row read optimization • Reduce GC pressure • Make internal nomenclature intuitive
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MORE PERFORMANCE IMPROVEMENTS IN CASSANDRA 3.0
Memory related • Support direct buffer decompression for reads
(CASSANDRA-8464) • Avoid memory allocation when searching index summary
(CASSANDRA-8793) • Use preloaded jemalloc w/ Unsafe (CASSANDRA-8714)
Throughput/CPU related • Improve concurrency of repair (CASSANDRA-6455, 8208) • Select optimal CRC32 implementation at runtime
(CASSANDRA-8614)
• plus many more
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SOMETHING ELSE
Windows • Cassandra 3.0 is tested on Windows • 3.0 works definitely better on Windows that 2.1 • But still some issues to solve
My personal recommendation • Use Cassandra on Linux
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CASSANDRA >= 3.1FEATURES
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CASSANDRA FUTURE
• Global indexes • More UDF related stuff
• Function based indexes • Use UDFs in filtering clauses • Distributed aggregates
• RAMP transactions • More internal improvements and optimizations • Expect Thrift to disappear
• Remember:Everything is subject to change if not released ;)
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CASSANDRA COMMUNITY
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CASSANDRA COMMUNITY
• People writing code (of course ;) ) • People doing talks and presentations • People active on social media
(Twitter, LinkedIn, SlideShare, YouTube) • People active on mailing list • People working in the background
• AND YOU !
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HINTS
• Use the material provided by • DataStax • Planet Cassandra • ”the community”
• on/via • YouTube • SlideShare • DataStax academy • Webinars • Meetups
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APACHE CASSANDRA"VS." DATASTAX ENTERPRISE
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APACHE CASSANDRA VS. DATASTAX ENTERPRISE 62
• Apache Cassandra is open-source • Support via user mailing list and tickets • No commercial support
• DataStax Enterprise uses Apache Cassandra • Adds graph database • Adds enhanced security • Adds analytics (Spark + Hadoop) • Adds search (SolR) • Adds commercial support
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ONE MORE THING… :)
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CASSANDRA-ON-MESOS
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APACHE MESOS
Program against your datacenter like it’s a single pool of resources
Apache Mesos abstracts CPU, memory, storage, and other compute resources away from machines (physical or virtual), enabling fault-tolerant and elastic distributed systems to easily be built and run effectively.
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MESOSPHERE DCOS 66
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APACHE MESOS 67
• Run killer applications and services like Spark, Kafka and Cassandra
• In your own data center • On Amazon EC2 • On Google GCE
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CASSANDRA-ON-MESOS 68
• Allows to run Apache Cassandra on Apache Mesos
• Developed by and
• Allows to spawn your Cassandra Cluster with a single command on Mesos
• Expect a first release-candidate this week !