Managing a

Cassandra Cluster:

Lessons Learned from 3M+

Node-Hours of Experience

19 April 2016

Agenda

About Instaclustr & presenters

Foundation practices for a happy cluster

The most common Cassandra issues and how to avoid them

Important monitoring and health-check procedures

Q & A

About Instaclustr

Cassandra, Spark and Zeppelin Managed Service on AWS, Azure and SoftLayer 500+ nodes

>80 clusters from 3 nodes to 50+

>3M node-hours of C* experience

Cassandra and Spark Consulting & Support

Ben Slater Chief Product Officer

Aleks Lubiejewski VP Consulting & Security

(prev VP Support)

Foundation Practices

What do I need to get right at the beginning to have a happy cluster?

Pick the right Cassandra version

Most stable -> Cassandra 2.1 (or, better yet, DSE)

Want the latest features and can live with cutting edge or have a few months until production -> Cassandra 3.x Odd numbers (eg 3.5) should be more stable as these are defect-fix releases

Cassandra 2.2: same end of life as Cassandra 2.1, no DSE version (yet?). Only recommended if you really want the new features but dont want to jump to 3.x.

Appropriate Hardware Configuration

Solid State Disks

Lots of memory

Can use EBS on AWS (and equivalent on other platforms) but needs very careful attention to configuration

For the cloud, we prefer more, smaller nodes: In AWS, m4.xlarge with 800G or 1.6TB are our standard building blocks Smaller proportionate impact of failure of a node Reasonable time to replace a failed node or add new ones

Estimating Costs in the Cloud Cost Description Driver Instances Cost of the base compute instances (eg m4.xl). Number and size of nodes in the cluster.

EBS Volume Cost of attached EBS volumes (where applicable) Size of the EBS volume (eg 400GB)

Network Public IP In/Out

Loading/retrieving data via public IP Only applicable if accessing via Public IP: dependant on number of Cassandra read/writes in a month and transaction size.

Network Interzone In/Out

Cross-availability zone communication within the cluster

Transaction volume and size, consistency factor used for reads

Network VPC In/Out

Loading/retrieving data via a peered VPC Only applicable if accessing via Peered VPC: dependant on number of Cassandra read/writes in a month and transaction size.

S3 Storage S3 space for storing backups Volume of data, length of backup retention, deduplication of backup files/data

S3 Operations S3 calls for storing backups Number of sstables (volume of data + compaction strategy), backup strategy

S3 Data Transfer Out S3 retrieval data transfer cost Only applicable if you need to copy data from S3 to a region other than US East to restore a backup.

EBS and network costs can exceed instance cost in some circumstances

CassandraSpecific Load Testing

Short load tests from the application side can be misleading.

Need to consider: Is there enough data on disk to overflow the operating system file cache?

Does your data reflect production distributions, particularly for primary key fields?

Are you performing deletes/updates to test for impact of tombstones (virtual deletes)?

If you are using cassandra-stress, do you understand the available options and their impacts?

NetworkTopologyStrategy, RF=3,CL=Quorum NetworkTopologyStrategy

Most future proof strategy

Allows use of multi-dc Can be very useful for cluster operations such as upgrades and splitting out tables

Replication Factor = 3, Consistency Level = Quorom Provides expected behaviour for most people:

Strong consistency

Availability through failure of a replica

Other settings are valid for many uses cases but need careful consideration of the impacts

Most Common Issues

What are the most common causes when things go wrong in production?

Data Modelling Issues

Partition Keys Cassandra primary keys consist of a partition key and a clustering key.

Eg PRIMARY KEY ((c1, c2), c3, c4)

Partition keys determine how data is distributed around the nodes

1 node per partition, many partitions per node

Need to ensure there are are a large number of partitions with a reasonable number of rows per partitions

Small number or very uneven partitions defeat the basic concepts of Cassandra scaling

Very large partitions can causes issues with garbage collection and excess disk usage

Data Modelling Issues (2)

Tombstones Tombstones are entries created to mark the fact that a record has been deleted

(updates to primary key will also cause tombstones)

By default, tombstones are retained for 10 days before being removed by compactions

High ratios of tombstones to live data can cause significant performance issues

Secondary Indexes Secondary Indexes are useful for a limited set of cases

only index low (but not too low) cardinality columns;

dont index columns that are frequently updated or deleted

Poor use of secondary indexes can result in poor read performance and defeat scalability

Other Issues

Write Overload Cluster may be able to initial handle a write workload but then fail to keep up

as compactions kick in

The effects can go beyond slow latency and cause crashes

Garbage Collection (GC) Long garbage collection pauses can often cause issues

Typically a symptom of partitions being too big or general overload of cluster

Tuning GC settings and heap allocations can help but need to address root cause also

Running out of capacity

Cassandra scales infinitely but processing capacity is used when adding new nodes to a cluster.

Therefore, you need to add capacity well before existing capacity is exhausted.

This applies to both disk and processor/memory.

Fundamental Monitoring & Health Check Procedures

How do I get advanced warning if my cluster is going to hit issues?

Monitoring Basic Metrics (OS)

Disk usage less than 70% under normal running is a good starting guide

this allows for bursts of usage by compactions and repairs

extreme cases may required 50% free space

Levelled compaction strategy and data spread across multiple column families can allow higher disk usage

CPU Usage again, 70% utilization is a reasonable target

Keep a look out for high iowait indicates storage bottleneck

Monitoring Basic Metrics (C*)

Read/Write Latency closely tied to user experience for most use cases

monitor for significant changes

be aware that read latency can very greatly depending on number of rows returned

distinguish between changes impacting a specific column family (likely data modelling issues) and changes impacting a specific node (hardware or capacity issues)

Pending Compactions increase numbers of pending compactions indicates that a node is not

keeping up with workload

Cassandra Logs

Regularly inspecting Cassandra logs for warnings and errors is important for picking up issues. The issues you will find include: large batch warnings

compacting large partitions warnings

reading excess tombstones warnings

plenty more!

Apr 18 08:00:27 ip-10-224-111-138.ec2.internal docker[15521]: [Native-Transport-Requests:22756] WARN

org.apache.cassandra.cql3.statements.BatchStatement Batch of prepared statements for [ks.col_family] is of size

59000, exceeding specified threshold of 5120 by 53880.

ip-10-224-169-153.eu-west-1.compute.internal docker[25837]: WARN o.a.c.io.sstable.SSTableWriter Compacting large

partition ks/col_family:c7d65814-1a58-4675-ad54-d6c92e10d1d7 (405357404 bytes)

Mar 29 11:55:26 ip-172-16-151-148.ec2.internal docker[30099]: WARN o.a.c.db.filter.SliceQueryFilter Read 3563 live

and 3520 tombstone cells in ks.col_family for key: 4364012 (see tombstone_warn_threshold). 5000 columns were

requested, slices=[2016-03-28 11:55Z:!-]

cfstats and cfhistograms

The Cassandra nodetool tool has many commands that help diagnose issues.

nodetool cfstats and cfhistograms are two of the most important

These tools can help you see: Large and uneven partitions

Excess tombstones

Too many sstables per read

read and write latency by keyspace and column family

many more

Summary

Cassandra is an incredibly reliable and scalable technology

if

you design and build correctly from the start

and follow basic management procedures.

Thank you for listening. QUESTIONS? Contact:

www.instaclustr.com

Hiro Komatsu hiro.komatsu@instaclustr.com

Ben Slater ben.slater@instaclustr.com

Aleks Lubiejewski aleks@instaclustr.com

http://www.instaclustr.com/mailto:hiro.komatsu@instaclustr.commailto:ben.slater@instaclustr.commailto:aleks@instaclustr.com