Real world capacity

Real world capacity planning: Cassandra on blades and big iron July 2011

About me
Hadoop System Admin @ media6degrees
Watch cassandra servers as well
Write code (hadoop filecrusher)
Hive Committer
Variable substitution, UDFs like atan, rough draft of c* handler
Epic Cassandra Contributor (not!)
CLI should allow users to chose consistency level
NodeCmd should be able to view Compaction Statistics
Self proclaimed president of Cassandra fan club
Cassandra NYC User Group
High Performance Cassandra Cookbook

Media6 Degrees
Social Targeting in online advertising
Real Time Bidding - A dynamic auction process where each impression is bid for in (near) real time
Cassandra @ work storing:
Visit Data
Ad History
Id Mapping
Multiple Data Centers (home brew replication)
Back end tools hadoop (Data mining, bulk loads)
Front end tomcat, mysql + cassandra (lookup data)

What is this talk about?
Real World Capacity Planning
Been running c* in production > 1 year
Started with a hand full of nodes also running tomcat and Replication Factor 2!
Grew data from 0-10 TB data
Grew from 0-751,398,530 reads / day
All types of fun along the way

Using puppet, chef... from day 1
“ I am going to chose Cassandra 0.6.0-beta-1 over 0.5.x so I am future proof” -- Famous quote by me
Cassandra is active
new versions are coming
Rolling restarts between minors
But much better to get all to same rev quickly
New nodes are coming do not let them:
start with the wrong settings
fail because you forgot open file limits, etc

Calculating Data size on disk
SSTable format currently not compressed
Repairs, joins, and moves need “wiggle room”
Smaller keys and column names save space
Enough free space to compact your largest column family
Snapshots keep SSTables around after compaction
Most *Nix files systems need free space to avoid performance loss to fragmentation!

Speed of disk
The faster the better!
But faster + bigger gets expensive and challenging
RAID0
Faster for streaming
not necessarily seeking
Fragile, larger the stripe, higher chance of failure
RAID5
Not as fast but survives disk failure
Battery backed cache helps but is $$$
The dedicated commit log decision

Disk formatting
ext4 everywhere
Deletes are much better then ext3
Noticeable performance as disks get full
A full async mode for risk takers
Obligatory noatime fstab setting
using multiple file systems can result in multiple caches (check slabtop)
Mention XFS

Memory
Garbage collection is on a separate thread(s)
Each request creates temporary objects
Cassandra's fast writes go to Memtables
You will never guess what they use :)
Bloom filter data is in memory
Key cache and Row cache
For low latency RAM must be some % of data
RAM not used by process is OS cache

CPU
Workload could be more disk then CPU bound
High load needs a CPU to clean up java garbage
Other then serving requests, compaction uses resources

Different workloads
Structured log format of C* has deep implications
Is data written once or does it change over time?
How high is data churn?
How random is the read/write pattern?
What is the write/read percentage?
What are your latency requirements?

Large Disk / Big Iron key points
RAID0 mean time to failure with bigger stripes
Java can not address large heaps well
Compactions/Joins/repairs take a long time
Lowers agility when joining a node could take hours
Maintaining high RAM to Data percentage costly IE 2 machines with 32GB vs 1 machine with 64GB
Capacity heavily diminished with loss of one node

Blade server key points
Management software gives cloud computing vibe
Cassandra internode traffic on blade back plane
Usually support 1-2 on board disk SCSI/SSD
Usually support RAM configurations up to 128G
Single and duel socket CPU
No exotic RAID options

Schema lessons
You only need one column family. not always true
Infrequently read data in the same CF as frequently data compete for “cache”
Separating allows employing multiple cache options
Rows that are written or updated get fragmented

Capacity Planning rule #1 Know your hard drive limits

Capacity Planning rule #2 Writes are fast, until c* flushes and compacts so much, that they are not

Capacity Planning rule #3
Row cache is fools gold
Faster then a read from disk cache
Memory use (row key + columns and values)
Causes memory pressure (data in and out of mem)
Fails with large rows
Cold on startup

Do not upgrade tomorrow what you can upgrade today
Joining nodes is intensive on the cluster
Do not wait till c* disks are 99% utilized
Do not get 100% benefit of new nodes until neighbors are cleaned
Doubling nodes results in less move steps
Adding RAM is fast and takes heat of hard disk

Know your traffic patterns better then yourself

The use case: Dr. Real Time and Mr. Batch

Dr. Real Time
Real time bidding needs low latency
Peak traffic during the day
Need to keep a high cache hit rate
Avoid compact, repair, cleanup, joins

Dr. Real Time's Lab
Experiments with Xmx vs VFS caching
Experiments with cache sizing
Studying graphs as new releases and features are added
Monitoring dropped messages, garbage collection
Dr. Real Time enjoys lots of memory for GB of data on disk
Enjoys reading (data), writing as well
Nice sized memtables help to not pollute vfs cache

Mr. Batch
Night falls and users sleep
Batch/Back loading data (bulk inserts)
Finding and removing old data (range scanning)
Maintenance work (nodetool)

Mr. Batch rampaging through the data
Bulk loading
Write at quorum, c* work harder on front end
Turning off compaction
For short burst fine, but we are pushing for hours
Forget to turn it back on SSTable count gets bad fast
Range scanning to locate and remove old data
Scheduling repairs and compaction
Mr. Batch enjoys tearing through data
Writes, tombstones, range scanning, repairs
Enjoys fast disks for compacting

Questions
???

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Real world capacity

by Edward Capriolo on Jul 12, 2011

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Real world capacity — Presentation Transcript