Let's have a brief look of Cassandra. By Boris Yen byan@ruckuswireless.com

1. Talk About Apache Cassandra
Boris Yen
byan@ruckuswireless.com

2. Outline
• Overview
• Architecture Overview
• Partitioning and Replication
• Data Consistency

3. 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

4. Overview
• High Performance
http://blog.cubrid.org/dev-platform/nosql-benchmarking/
• Schema-less (Not completely true)
– Need to provide basic settings for each column family.

5. 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

6. Architecture Overview
Standard CF Super CF
{ {
Key1: { Key1: {
column1: value, super_column1: {
column2: value subColumn1: value,
}, subColumn2: value
Key2: { },
column1: value, super_column2: {
column2: value subColumn1: value,
} subColumn2: value
} }
},
Recommended. Super Key2: {
columns could be somehow super_column1: {
replaced by composite subColumn1: value,
columns. subColumn2: value
}
}

7. 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.

8. Architecture Overview
• Data write path
Data Commitlog Memtable
Flushed
SSTable

9. 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.

10. 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.

11. Partitioning
“boris” is inserted here
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

12. 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

13. 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.

14. 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.

15. Replication
RF=3
“boris” is inserted here
Data
Data is inserted and
assigned a row key in a
column family. “boris” is inserted here “boris” is inserted here
{
boris:{
first name: boris,
last name: Yen
}
Copy of row is replicated across
}
various nodes based on the assigned
replication factor

16. 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.

17. Replication - Network Topology Strategy
RF={DC1:2, DC2:2}
http://www.datastax.com/docs/1.0/cluster_architecture/replication

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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

24. 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.

25. 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%

26. 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

27. 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

28. 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

29. 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

30. Q&A

31. We are hiring people
• If you are interesting in what we are doing,
please contact us.