Cassandra 3.0 Advanced PreviewPatrick McFadin @PatrickMcFadin

Chief Evangelist for Apache Cassandra

Intro

1.Developer friendly 2.Pay down a lot of technical debt 3.Push performance even higher

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Developer Productivity

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User Defined Functions

• Counter table • User clicks on a number of stars • rating_counter = How many clicks • rating_total = Cumulative amount of stars

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CREATE TABLE video_rating ( videoid uuid, rating_counter counter, rating_total counter, PRIMARY KEY (videoid) );

User Defined Functions

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CREATE TABLE video_rating ( videoid uuid, rating_counter counter, rating_total counter, PRIMARY KEY (videoid) );

public long getRatingForVideo(UUID videoId) { BoundStatement bs = getRatingByVideoPreparedStatement.bind(videoId); ResultSet rs = session.execute(bs); Row row = rs.one(); // Get the count and total rating for the video long total = row.getLong("rating_total"); long count = row.getLong("rating_counter"); // Divide the total by the count and return an average return (total / count);}

User Defined Functions

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CREATE TABLE video_rating ( videoid uuid, rating_counter counter, rating_total counter, PRIMARY KEY (videoid) );

public long getRatingForVideo(UUID videoId) { BoundStatement bs = getRatingByVideoPreparedStatement.bind(videoId); ResultSet rs = session.execute(bs); Row row = rs.one(); // Get the count and total rating for the video long total = row.getLong("rating_total"); long count = row.getLong("rating_counter"); // Divide the total by the count and return an average return (total / count);}

Application code?

User Defined Functions

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CREATE OR REPLACE FUNCTION averageRating ( rating_counter counter, rating_total counter ) RETURNS FloatLANGUAGE javaAS ' return Float.valueOf(rating_total.floatValue() / rating_counter.floatValue()); ';

Function Name CQL TypeObject return type

Java Code

User Defined Functions

• Add to your CQL statement!8

> SELECT averageRating(rating_counter, rating_total) AS avg FROM video_rating WHERE videoid = 99051fe9-6a9c-46c2-b949-38ef78858dd0;

videoid | rating_counter | rating_total--------------------------------------+----------------+-------------- 99051fe9-6a9c-46c2-b949-38ef78858dd0 | 3 | 12

avg ----- 4

User Defined Functions - Fine print

• “Pure” functions • Nothing outside of input parameters

• Return types are only objects. No primitives • Method signatures on parameter type

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User Defined Function Language Support

• Java • JavaScript

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• Scala • Groovy • Jython • JRuby

Primary Languages

Optional Languages

JSON Support

• Table to store a video • TYPE to store metadata

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CREATE TYPE video_metadata ( height int, width int, video_bit_rate set<text>, encoding text);

CREATE TABLE videos ( videoid uuid, userid uuid, name varchar, description varchar, location text, location_type int, preview_thumbnails map<text,text>, tags set<varchar>, metadata set <frozen<video_metadata>>, added_date timestamp, PRIMARY KEY (videoid) );

JSON Support

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INSERT INTO videos (videoid, name, userid, description, location, location_type, preview_thumbnails, tags, added_date, metadata) VALUES (49f64d40-7d89-4890-b910-dbf923563a33,'The World''s Next Top Data Model',9761d3d7-7fbd-4269-9988-6cfd4e188678, 'Third in a three part series for Cassandra Data Modeling','http://www.youtube.com/watch?v=HdJlsOZVGwM',1, {'YouTube':'http://www.youtube.com/watch?v=HdJlsOZVGwM'},{'cassandra','data model','examples','instruction'},'2013-06-11 11:00:00', {{ height: 480, width: 640, encoding: 'MP4', video_bit_rate: {'1000kbs', '400kbs'}}});

Decompose into standard insert

OR!

JSON Support

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INSERT INTO videos JSON'{ "videoid":"99051fe9-6a9c-46c2-b949-38ef78858dd0", "added_date":"2012-06-01 08:00:00.000", "description":"My cat likes to play the piano! So funny.", "location":"/us/vid/b3/b3a76c6b-7c7f-4af6-964f-803a9283c401", "location_type":1, "metadata":[ { "height":480, "width":640, "video_bit_rate":[ "1000kbs", "400kbs" ], "encoding":"MP4" } ], "name":"My funny cat", "preview_thumbnails":{ "10":"/us/vid/b3/b3a76c6b-7c7f-4af6-964f-803a9283c401" }, "tags":[ "cats", "lol", "piano" ], "userid":"d0f60aa8-54a9-4840-b70c-fe562b68842b"}';

One block of JSON

OR!

JSON Support

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INSERT INTO videos (videoid, name, userid, description, location, location_type, preview_thumbnails, tags, added_date, metadata) VALUES (99051fe9-6a9c-46c2-b949-38ef78858dd0,'My funny cat',d0f60aa8-54a9-4840-b70c-fe562b68842b, 'My cat likes to play the piano! So funny.','/us/vid/b3/b3a76c6b-7c7f-4af6-964f-803a9283c401',1,{'10':'/us/vid/b3/b3a76c6b-7c7f-4af6-964f-803a9283c401'},{'cats','piano','lol'},'2012-06-01 08:00:00',fromJson(' [{ "height":480, "width":640, "video_bit_rate":[ "1000kbs", "400kbs" ], "encoding":"MP4" }]') );

Just a block at a time

Get JSON data

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[json] ------------------------------------------------------------------'{ "videoid":"99051fe9-6a9c-46c2-b949-38ef78858dd0", "added_date":"2012-06-01 08:00:00.000", "description":"My cat likes to play the piano! So funny.", "location":"/us/vid/b3/b3a76c6b-7c7f-4af6-964f-803a9283c401", "location_type":1, "metadata":[ { "height":480, "width":640, "video_bit_rate":[ "1000kbs", "400kbs" ], "encoding":"MP4" } ], "name":"My funny cat", "preview_thumbnails":{ "10":"/us/vid/b3/b3a76c6b-7c7f-4af6-964f-803a9283c401" }, "tags":[ "cats", "lol", "piano" ], "userid":"d0f60aa8-54a9-4840-b70c-fe562b68842b"}';

SELECT JSON * FROM videos;

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Indexing

Global Indexes

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CREATE TABLE videos ( videoid uuid, userid uuid, name varchar, description varchar, location text, location_type int, preview_thumbnails map<text,text>, tags set<varchar>, metadata set <frozen<video_metadata>>, added_date timestamp, PRIMARY KEY (videoid) );

CREATE TABLE videos_by_tag ( tag text, videoid uuid, added_date timestamp, name text, preview_image_location text, tagged_date timestamp, PRIMARY KEY (tag, videoid) );

Application maintained consistency

Global Indexes

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CREATE TABLE videos ( videoid uuid, userid uuid, name varchar, description varchar, location text, location_type int, preview_thumbnails map<text,text>, tags set<varchar>, metadata set <frozen<video_metadata>>, added_date timestamp, PRIMARY KEY (videoid) );

CREATE GLOBAL INDEX tags_index ON videos (tag, videoid) INCLUDE (name, added_date, preview_thumbnails)

CREATE TABLE videos_by_tag ( tag text, videoid uuid, added_date timestamp, name text, preview_image_location text, tagged_date timestamp, PRIMARY KEY (tag, videoid) );

Global Indexes

• Separate Cassandra managed table • Inserts • Updates

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More Indexes!

• Partial Indexes - Postponed until 3.1 • Functional Indexes - using a UDF in an index

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CREATE INDEX ON user_rating averageRating(rating_counter, rating_total);

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File System

Hints to Raw Files

• Pre 3.0 hints stored in table • Create load on entire write path • …and read path • …and compaction

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CREATE TABLE system.hints ( target_id uuid, hint_id timeuuid, message_version int, mutation blob, PRIMARY KEY (target_id, hint_id, message_version) ) WITH COMPACT STORAGEAND CLUSTERING ORDER BY (hint_id ASC, message_version ASC);

Hints to Raw Files

• Hints now written to a local file • Replays direct from disk • Bulk streamed to endpoints

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CREATE TABLE system.hints ( target_id uuid, hint_id timeuuid, message_version int, mutation blob, PRIMARY KEY (target_id, hint_id, message_version) ) WITH COMPACT STORAGEAND CLUSTERING ORDER BY (hint_id ASC, message_version ASC);

Windows Compatibility - The Problem

• Java file management on Windows is… different • File delete’s are not possible • Hard links - Broke • Snapshots - Broke • Memory Mapped I/O - Broke

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Windows Compatibility - 3.0

• Re-tooling of critical file functions • Extensive use of FILE_SHARE_DELETE from JDK7 • Launch now in PowerShell • CCM now supports windows

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Storage Engine Changes

• Now infamous CASSANDRA-8099 • Technical debt from Thrift • Move from Thrift centric to CQL centric storage

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Pre 3.0 Storage Engine Format

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2005:12:1:102005:12:1:92005:12:1:82005:12:1:7

5F22A0BC

Partition Key Clustering Columns

F2B3652CFFB3652D7AB3652C

PRIMARY KEY (userId,added_date,videoId)

A12378E55F5A32

3.0 Format

• Partition header stores column names • Row stores clustering values • No duplicated values

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Partition Key Column Names

Clustering Values Column Values

Clustering Values Column Values

Partition Header Row Row

Clustering Values Column Values

Row

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Performance

Commit Log Compression

• Pre 3.0 commit log writes

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Commit Log Compression

• Pre 3.0 commit log writes

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Commit Log Compression

• Pre 3.0 commit log writes

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Commit Log Compression

• Pre 3.0 commit log writes

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Commit Log Compression

• Pre 3.0 commit log writes

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Commit Log Compression

• Pre 3.0 commit log writes

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Commit Log Compression

• Pre 3.0 commit log writes

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Commit Log Compression

• Segments are compressed by time interval • Higher throughput under high writes

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Commit Log Compression

• Segments are compressed by time interval • Higher throughput under high writes

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Commit Log Compression

• Segments are compressed by time interval • Higher throughput under high writes

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Smaller but significant changes

• Direct buffer decompression of reads • Avoiding memory allocation on Index Summary search • Repair concurrency improvements • Optimal CRC32 implementation at runtime

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Security

Role Based Access Control

• Expands on User based auth in 1.2 • Requires the internal auth to be enabled

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CREATE ROLE supervisor; GRANT MODIFY ON user_credentials TO supervisor;

When will it ship?

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Maybe June

When 8099 is finished, it ships

Thank you!

Questions Follow me on Twitter for more

@PatrickMcFadin