Talk About Apache Cassandra

Boris Yenbyan@ruckuswireless.com

Outline

• Overview• Architecture Overview• Partitioning and Replication• Data Consistency

Overview

• Distributed– Data partitioned among all nodes

• Extremely Scalable– Add new node = Add more capacity– Easy to add new node

• Fault tolerant– All nodes are the same– Read/Write anywhere– Automatic Data replication

Overview

• High Performance

• Schema-less (Not completely true)– Need to provide basic settings for each column family.

http://blog.cubrid.org/dev-platform/nosql-benchmarking/

Architecture Overview

• Keyspace– Where the replication strategy and replication factor is

defined– RDBMS synonym: Database

• Column family– Standard (recommended) or Super– Lots of settings can be defined– RDBMS synonym: Table

• Row/Record– Indexed by Key. Columns might be indexed as well– Column name are sorted based on the comparator– Each column has its own timestamp

Architecture OverviewStandard CF{ Key1: { column1: value, column2: value }, Key2: { column1: value, column2: value }}

Recommended. Super columns could be somehow replaced by composite columns.

Super CF{ Key1: { super_column1: { subColumn1: value, subColumn2: value }, super_column2: { subColumn1: value, subColumn2: value } }, Key2: { super_column1: { subColumn1: value, subColumn2: value }}

Architecture Overview• Commit log– Used to capture write activities. Data durability is

assured.• Memtable– Used to store most recent write activities.

• SSTable– When a memtable got flushed to disk, it becomes

a sstable.

Architecture Overview

• Data write path

Commitlog MemtableData

SSTable

Flushed

Architecture Overview

• Data read path– Search Row cache, if the result is not empty, then

return the result. No further actions are needed.– If no hit in the Row cache. Try to get data from

Memtable(s) and SSTable(s). Collate the results and return.

Partitioning and Replication

• In Cassandra, the total data managed by the cluster is represented as a circular space or ring.

• The ring is divided up into ranges equal to the number of nodes, with each node being responsible for one or more ranges of the overall data.

• Before a node can join the ring, it must be assigned a token. The token determines the node’s position on the ring and the range of data it is responsible for.

Partitioning

2…

4…6…

8…

0

Data

Data is inserted and assigned a row key in a column family.

{ boris:{ first name: boris, last name: Yen }}

Data placed on the node based on its row key

“boris” is inserted here

Partitioning Strategies

• Random Partitioning– This is the default and recommended strategy. Partition

data as evenly as possible across all nodes using an MD5 hash of every column family row key

• Order Partitioning– Store column family row keys in sorted order across all

nodes in the cluster.– Sequential writes can cause hot spots– More administrative overhead to load balance the cluster– Uneven load balancing for multiple column families

Setting up data Partitioning

• The data partitioning strategy is controlled via the partitioner option inside cassandra.yaml file

• Once a cluster in initialized with a partitioner option, it can not be changed without reloading all of the data in the cluster.

Replication

• To ensure fault tolerance and no single point of failure, you can replicate one or more copies of every row across nodes in the cluster

• Replication is controlled by the parameters replication factor and replication strategy of a keyspace

• Replication factor controls how many copies of a row should be store in the cluster

• Replication strategy controls how the data being replicated.

Replication

2…

4…6…

8…

0

Data

Data is inserted and assigned a row key in a column family.

{ boris:{ first name: boris, last name: Yen }}

Copy of row is replicated across various nodes based on the assigned replication factor

“boris” is inserted here

“boris” is inserted here“boris” is inserted here

RF=3

Replication Strategies

• Simple Strategy– Place the original row on a node determined by the

partitioner. Additional replica rows are placed on the new nodes clockwise in the ring.

• Network Topology Strategy– Allow replication between different racks in a data center

and or between multiple data centers– The original row is placed according the partitioner.

Additional replica rows in the same data center are then placed by walking the ring clockwise until a node in a different rack from previous replica is found. If there is no such node, additional replicas will be placed in the same rack.

Replication - Network Topology Strategy

RF={DC1:2, DC2:2}

http://www.datastax.com/docs/1.0/cluster_architecture/replication

Replication Mechanics

• Cassandra uses a snitch to define how nodes are grouped together within the overall network topology, such as rack and data center groupings.

• The snitch is defined in the cassandra.yaml

Replication Mechanics - Snitches

• Simple Snitch– The default and used for simple replication strategy

• Rack Inferring Snitch– Infers the topology of the network by analyzing the node IP

addresses. This snitch assumes that the second octet identifies the data center where a node is located, and third octet identifies the rack

• Property File Snitch– Determines the location of nodes by referring to a user-

defined file, cassandra-topology.properties• EC2 Snitch– Is for deployments on Amazon EC2 only

Data Consistency

• Cassandra supports tunable data consistency• Choose from strong and eventual consistency

depending on the need• Can be done on a per-operation basis, and for

both reads and writes.• Handles multi-data center operations

Consistency Level for Writes• Any

– A write must succeed on any available node (hint included)• One

– A write must succeed on any node responsible for that row (either primary or replica)

• Quorum– A write mush succeed on a quorum of replica nodes (RF/2 + 1)

• Local_Quorum– A write mush succeed on a quorum of replica nodes in the same data

center as the coordinator node.• Each_Quorum

– A write must succeed on a quorum of replica nodes in all data centers• All

– A write must succeed on all replica nodes for a row key

Consistency Level for Reads

• One– Reads from the closest node holding the data

• Quorum– Returns a result from a quorum of servers with the most recent

timestamp for the data• Local_Quorum

– Returns a result from a quorum of servers with the most recent timestamp for the data in the same data center as the coordinator node

• Each_Quorum– Returns a result from a quorum of servers with the most recent

timestamp in all data centers• All

– Returns a result from all replica nodes for a row key

Built-in Consistency Repair Features

• Read Repair– When a read is done, the coordinator node

compares the data from all the remaining replicas that own the row in the background, and If they are inconsistent, issues writes to the out-of-date replicas to update the row.

• Anti-Entropy Node Repair• Hinted Handoff

What is New in 1.0

• Column Family Compression– 2x-4x reduction in data size– 25-35% performance improvement on reads– 5-10% performance improvement on writes

• Improved Memory and Disk Space Management– Off-heap row cache– Storage engine self-tuning– Faster disk space reclamation

• Tunable Compaction Strategy– Support LevelDB style compaction algorithm that can be

enabled on a per-column family basis.

What is New in 1.0

• Cassandra Windows Service• Improved Write Consistency and Performance– Hint data is stored more efficiently– Coordinator nodes no longer need to wait for the

failure detector to mark a node as down before saving hints for unresponsive nodes.• Running a full node repair to reconcile missed writes is

not necessary. Full node repair is only necessary when simultaneous multi-node fails o losing a node entirely• Default read repair probability has been reduced from

100% to 10%

Anti-Patterns

• Non-Sun JVM• CommitLog+Data on the same Disk– Does not apply to SSDs or EC2

• Oversized JVM heaps– 6-8 GB is good– 10-12 is possible and in some circumstances

“correct”– 16GB == max JVM heap size– > 16GB => badness

http://www.slideshare.net/mattdennis/cassandra-antipatterns

Anti-Patterns

• Large batch mutations– Timeout => entire mutation must be retried =>

wasted work– Keep the batch mutations to 10-100 (this really

depends on the HW)• Ordered partitioner– Creates hot spots– Requires extra cares from operators

• Cassnadra auto selection of tokens– Always specify your initial token.

http://www.slideshare.net/mattdennis/cassandra-antipatterns

Anti-Patterns

• Super Column– 10-15 percent performance penalty on reads and

writes– Easier/Better to use to composite columns

• Read Before write• Winblows

http://www.slideshare.net/mattdennis/cassandra-antipatterns

Want to Learn More

• http://www.datastax.com/resources/tutorials• http://www.datastax.com/docs/1.0/index

P.S. Most of the content in this presentation is actually coming from the websites above

Q&A

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