introduciton to apache cassandra for java developers (javaone)

PowerPoint Presentation

Apache Cassandra

An Introduction for Java Developers

Nate [email protected]@zznate

What is Apache Cassandra?

CAP Theorem

ConsistencyAvailability Partition Tolerance

Though shalt have but 2

- Conjecture made by Eric Brewer in 2000- Published as formal proof in 2002- See: http://en.wikipedia.org/wiki/CAP_theorem for more

Apache Cassandra Concepts

- Explicit choice of partition tolerance and availability. Consistency is tunable.- No read before write- Merge on read- Idempotent- Schema Optional- All nodes share the same roll- Still performs well with larger-than-memory data sets

Generally complements another system(s)

(Not intended to be one-size-fits-all)

*** You should always use the right tool for the right job anyway

How does this differ from an RDBMS?

How does this differ from an RDBMS?

Substantially.

vs. RDBMS - No Joins

Unless: - you do them on the client - you do them via Map/Reduce

vs. RDBMS - Schema Optional

(Though you can add meta information for validation and type checking)

*** Supports secondary indexes too: WHERE state = 'TX'

vs. RDBMS - Prematerialized and Transaction-less

- No ACID transactions - Limited support for ad-hoc queries

vs. RDBMS - Prematerialized and Transaction-less

- No ACID transactions - Limited support for ad-hoc queries

*** You are going to give up both of these anyway when you shard an RDBMS ***

vs. RDBMS - Facilitates Consolidation

It can be your caching layer * Off-heap cache (provided you install JNA)

It can be your analytics infrastructure * true map/reduce * pig driver * hive driver coming soon

vs. RDBMS - Shared-Nothing Architecture

Every node plays the same role: no masters, no slaves, no special nodes

*** No single point of failure

vs. RDBMS - Real Linear Scalability

Want 2x performance? Add 2x nodes.

*** 'No downtime' included!

vs. RDBMS - Performance

Reads on par with writes

Clustering

Clustering

Single node cluster (easy development setup)- one node owns the whole hash range

Clustering

Two node cluster- Key range divided between nodes

Clustering

Consistent Hashing: md5(zznate) = C

Clustering

Consistent Hashing FTW:- Ring ownership continuously gossiped between nodes- Any node can act as a coordinator to service client requests for any key * requests forwarded to the appropriate nodes by coordinator transparently to the client

Clustering

Client Read: get(zznate)md5 = C

Clustering Scale Out



Clustering - Multi-DC

Clustering - Reliability




Clustering - Multi-Datacenter

Clustering Multi-DC Reliability

Storage (Briefly)

Storage (Briefly)

Understanding the on-disk format is extremely helpful in designing your data model correctly

Storage - SSTable

- SSTables are immutable (Merge on read)- Newest timestamp wins

Storage Compaction

Merge SSTables keeping count down making Merge on Read more efficientDiscards Tombstones (more on this later!)

Data Model

Data Model

"...sparse, persistent, distributed, multi-dimensional sorted map."

(The Bigtable paper)

Data Model

Keyspace- Collection of Column Families

- Controls replication

Column Family

- Similar to a table

- Columns ordered by name

Data Model Column Family

Static Column Family- Model my object data

Dynamic Column Family

- Pre-calculated query results

Nothing stopping you from mixing them!

Data Model Static CF

zznate

driftx

thobbs

jbellis

password: *

password: *

password: *

name: Nate

name: Brandon

name: Tyler

password: *

name: Jonathan

site: datastax.com

Users

Data Model Prematerialized Query

Following

zznate

driftx

thobbs

jbellis

driftx:

thobbs:

driftx:

thobbs:

mdennis:

zznate

zznate:

pcmanus

xedin:

Data Model Prematerialized Query

Additional examples:Timeline of tweets by a userTimeline of tweets by all of the people a user is followingList of comments sorted by scoreList of friends grouped by state

API Operations

Five general categories

RetrievingWriting/Updating/Removing (all the same op!)Increment counters

Meta InformationSchema ManipulationCQL Execution

Using a Client

Hector Client:http://hector-client.org- Most popular Java client - In use at very large installations- A number of tools and utilities built on top- Very active community- MIT Licensed

*** like any open source project fully dependent on another open source project it has it's worts

Sample Project for Experimenting

https://github.com/zznate/cassandra-tutorialhttps://github.com/zznate/hector-examplesBuilt using Hector Really basic designed to be beginner level w/ very few moving partsModify/abuse/alter as needed

*** Descriptions of what is going on and how to run each example are in the Javadoc comments.

ColumnFamilyTemplate

Familiar, type-safe approach- based on template-method design pattern- generic: ColumnFamilyTemplate (K is the key type, N the column name type)

ColumnFamilyTemplate template = new ThriftColumnFamilyTemplate(keyspaceName, columnFamilyName, StringSerializer.get(), StringSerializer.get());

*** (no generics for clarity)


new ThriftColumnFamilyTemplate(keyspaceName, columnFamilyName, StringSerializer.get(), StringSerializer.get());

Key Format

Column Name Format- Cassandra calls this a comparator- Remember: defines column order in on-disk format


ColumnFamilyResult res = cft.queryColumns("zznate");

String value = res.getString("email");

Date startDate = res.getDate(startDate);

Key Format

Column Name Format


ColumnFamilyResult wrapper = template.queryColumns("zznate", "patricioe", "thobbs");

String nateEmail = wrapper.getString("email");

wrapper.next();

String patoEmail = wrapper.getString("email");

wrapper.next(); String tylerEmail = wrapper.getString("email");

Querying multiple rows and iterating over results


ColumnFamilyUpdater updater = template.createUpdater("zznate");

updater.setString("companyName","DataStax");updater.addKey("sergek");updater.setString("companyName","PrestoSports");

template.update(updater);

Inserting data with ColumnFamilyUpdater


template.deleteColumn("zznate", "notNeededStuff");template.deleteColumn("zznate", "somethingElse");template.deleteColumn("patricioe", "aDifferentColumnName");...template.deleteRow(someuser);

template.executeBatch();

Deleting Data with ColumnFamilyTemplate

Deletion

Deletion

Again: Every mutation is an insert!

- Merge on read

- Sstables are immutable

- Highest timestamp wins

Deletion As Seen by CLI

[default@Tutorial] list StateCity;Using default limit of 100

-------------------

RowKey: CA Burlingame

=> (column=650, value=33372e3537783132322e3334, timestamp=1310340410528000)

-------------------

RowKey: TX Austin






Deletion As Seen by CLI

[default@Tutorial] list StateCity;Using default limit of 100

-------------------

RowKey: CA Burlingame

-------------------

RowKey: TX Austin






Deletion FYI

mutator.addDeletion("202230", "Npanxx", city, stringSerializer);

Does not exist? You just inserted a tombstone!

Sending a deletion for a non-existing row:

[default@Tutorial] list Npanxx; Using default limit of 100

. . .

-------------------

RowKey: 202230

-------------------

. . .

Integrating with existing patterns


Yes.



Hector Object Mapper:

https://github.com/rantav/hector/wiki/Hector-Object-Mapper-%28HOM%29

Hector JPA:

https://github.com/riptano/hector-jpa


CQL: JDBC Driver and Pool in 1.0!

JdbcTemplate FTW!

Development Resources

Hector Documentation
http://hector-client.orgCassandra Maven Plugin
http://mojo.codehaus.org/cassandra-maven-plugin/CCM localhost cassandra cluster
https://github.com/pcmanus/ccmOpsCenter
http://www.datastax.com/products/opscenter

Cassandra AMIs
https://github.com/riptano/CassandraClusterAMI

Putting it Together

Take control of consistency

If you do need a high degree of consistency, use thresholds to trigger different behavior

- Bank account:

on values over $10,000, wait to here from all replicas

- Distributed Shopping Cart:

Show a confirmation page to verify order resolution

*** What is your appetite for risk?

Uniquely identify operations in the application

Facilitates idempotent behavior and out-of-order execution

Denormalization

The point of normalization is to avoid update anomalies

***But In an append-only system, we don't do updates

Summary

- Take advantage of strengths

- Look for idempotence and asynchronicity in your business processes

- If it's not in the API, you are probably doing it wrong

- Seek death is still possible if you model incorrectly

Questions

Nate [email protected]@zznate

Additional Resources

DataStax Documentation: http://www.datastax.com/docs/0.8/index

Apache Cassandra project wiki: http://wiki.apache.org/cassandra/

The Dynamo Paper

http://www.allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf

P. Helland. Building on Quicksand

http://arxiv.org/pdf/0909.1788

P. Helland. Life Beyond Distributed Transactions

http://www.ics.uci.edu/~cs223/papers/cidr07p15.pdf

S. Anand. Netflix's Transition to High-Availability Storage Systems

http://media.amazonwebservices.com/Netflix_Transition_to_a_Key_v3.pdf

The Megastore Paper

http://research.google.com/pubs/archive/36971.pdf

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