Cassandra Summit 2015: Real World DTCS For Operators

An Introduction
to CrowdStrike
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What Is Compaction?
• Cassandra write path:
– First the Commitlog
– Then the Memtable
– Eventually flushed to a SSTable
• Each SSTable is written exactly once
• Over time, Cassandra combines files
– Duplicate cells are merged
– Obsolete data is purged
• The algorithm Cassandra uses to determine when and how to combine
files is pluggable, and choosing the right strategy may be important at
scale
3© 2015. All Rights Reserved.

What Is Compaction?
• SizeTieredCompactionStrategy
– Each time min_threshold (4) files of the same size appear, combine them
into a new file
– Over time, you’ll naturally end up with a distribution of old data in large
files, new data in small files
– Deleted data in large files stays on disk longer than desired because
those files are very rarely compacted
4© 2015. All Rights Reserved.

SizeTieredCompactionStrategy
© 2015. All Rights Reserved. 5

SizeTieredCompactionStrategy
If each of the smallest blocks represent 1 day of data, and each write
had a 90 day TTL, when do you actually delete files and reclaim disk
space?

Why Compaction Strategy Matters
• We keep some data from sensors for a
fixed time period
• Processes
• DNS queries
• Files created
• It’s a LOT of data
• Talk tomorrow morning: One million
writes per second with 60 nodes
• We’re WELL past 60 nodes
• If we can’t delete it efficiently, costs go
way, way up

DateTieredCompactionStrategy
• Early tickets suggested creating a way to stop compacting cold
data
– CASSANDRA-5515 – track sstable coldness, stop compacting cold
sstables (measured by READ counts)
• CASSANDRA-6602 – optimize for time series specifically
– Solution provided by Björn Hegerfors from Spotify
– Use sstable’s min timestamp to find a target window
– Compact sstables within the same target
– Stop compacting sstables if max timestamp is older than a specified cutoff

DTCS In Pictures

DTCS Parameters
• max_sstable_age_days
• base_time_seconds
• timestamp_resolution
• Min_threshold
– Common to all compaction strategies
• Max Threshold
– Common to all compaction strategies

DTCS In Pictures

DTCS Benefits
In Theory…
• You can stop data compacting at a point you choose!
– max_sstable_age_days
• You can adjust the window size so that you can quickly expire
data when it’s approximately the size you want
– It’s not immediately intuitive, but you CAN calculate it (min_threshold and
base_time_seconds)
• We know cold data won’t be recompacted, so we can potentially
enable cold storage directories with cheaper disk
– CASSANDRA-8460 – patch available, I need to rebase

Do people consider DTCS Production Ready?
• It was added to 2.0 after 2.1 was out. Usually this means:
– Trivial and low risk, or
– Experimental and meant for advanced users only

– I challenge you to find documentation on which is true for DTCS

– I challenge you to find documentation on which is true for DTCS
• Spotify’s intro blog notes that they use it in production
• I’ve been told by a project committer that they feel DTCS is for
advanced users only, but I’ve never seen any public facing
messaging that normal users should avoid it
• It seems so easy, what could possibly go wrong…

DTCS Caveats
• The initial blogs give us some insight about what type of things
may not behave as intended
– “But something that works against the efforts of the strategy is writes with
highly out-of-order timestamps”
• How much is “highly out of order”?
– “Consider turning off read repairs. Anti-entropy repairs and hinted handoff
don’t incur as much additional work for DTCS and may be used like
usual.”

Out of order timestamps
• When an sstable gets flushed with an old timestamp in a new
table:
– The max timestamp is used to determine when to stop compacting, but
– The min timestamp is used to determine which other files will be
compacted with this sstable

• Windows are tiered, and they get bigger and bigger
• With default settings and 1 year of data, the largest window
covers 180 days
– This means even if most of the file is past max_sstable_age_days, you
can still end up compacting with a brand new sstable with read repaired
data
• “DTCS never stops compacting”
– Read repairs pull old data into new windows triggering
recompaction

• Windows are tiered, and they get bigger and bigger
• With default settings and 1 year of data, the largest window
covers 180 days
– This means even if most of the file is past max_sstable_age_days, you
can still end up compacting with a brand new sstable with read repaired
data
• “DTCS never stops compacting”
– Read repairs pull old data into new windows triggering recompaction
– Does that mean we better run repair?

Small SSTables from Repairs
(and other streaming operations)
• “If an SSTable contains timestamps that don’t match the time
when it was actually written to disk, it violates the size-to-age
correspondence that DTCS tries to maintain.”
• The suggestions on Spotify and Datastax blogs say run repair
more often than max_sstable_age_days, but that isn’t the only
cause of small sstables
– Bootstrap
– Decommission
– Bulk Loader

Real Pain:
If you can’t expand your cluster, what’s the point?
SSTable Count Per Node

Real Pain:
If you can’t expand your cluster, what’s the point?
Damn you, vnodes!

Well…

Small SSTables Shouldn’t Be Ignored
• If the small sstables are beyond max_sstable_age_days, they
won’t be compacted
– After all, that’s the point of max_sstable_age_days, right?
• If you raise max_sstable_age_days, the ever-growing DTCS
tiered windows will cause existing sstables to merge and get
much larger, negating one of the benefits of DTCS
• If you don’t raise max_sstable_age_days, you have to deal with
performance implications of ten thousand sstables
– Reduced somewhat by CASSANDRA-9882
– Before #9882, too many sstables could block flushing for a long time

Embarrassing Admission
• Our early bulk loading plan and bootstrapping procedure
acknowledged that sstables will be abandoned beyond
max_sstable_age_days
• We have python scripts that check the timestamps, and
manually submit compactions through JMX
forceUserDefinedCompaction()

Really Embarrassing Admission
acknowledged that sstables will be abandoned beyond
max_sstable_age_days
• Yes, really.

Really Embarrassing Admission
acknowledged and accepted that sstables will be abandoned
beyond max_sstable_age_days
• Yes, really.
• Does it actually scale?

When should you use DTCS?
• You TTL ALL of your data and writes come in order
• Fixed sized cluster and no plans for bulk loading, or rarely
changing cluster size and not using vnodes
– If you plan on growing, you better have a plan for small sstables
– If you do need to add/remove nodes, vnodes will cause far more small
sstables than single-token-per-node
• Extra space available for compaction
– You can’t rely on theoretical table sizes calculated with
max_sstable_age_days, because read repair, hints, etc, can force those
files to span much larger time ranges than you expect

Being Honest

What if?
• Do we really need max_sstable_age_days?
– The conventional logic is to use it to denote cold data, but we use it to
force window sizes
– If we give up tiering, and stick with fixed sized windows, do we need
max_sstable_age_days?
• Without tiering, can we swap base_time_seconds for more
intuitive configuration knob option?

TimeWindowCompactionStrategy
• Designed to be simple and efficient
– Group sstables into logical buckets
– STCS within each time window
– No more rolling re-compaction
– No more streaming leftovers
– No more confusing options, just Window Size + Window Unit
• “12 Hours”, “3 Days”, “6 Minutes”

• Submitted to Apache Cassandra as CASSANDRA-9666
• For now, we use it at Crowdstrike to clean up after streaming:
– echo "set -b
org.apache.cassandra.db:columnfamily=table,keyspace=keyspace,type=ColumnFamilies
CompactionStrategyClass
org.apache.cassandra.db.compaction.TimeWindowCompactionStrategy" | java -jar
jmxterm.jar -l $IP:$PORT
– It’s not an accident that the TWCS defaults use 1 day windows with
microsecond timestamp resolution, that matches our sstable needs, but
we think it’s a good default
• Patches (and Tests) Available for 2.1, 2.2, 3.0

• No more continuous compaction
• No more tiny streaming leftovers
• No more confusing options
– Just Window Size, Window Unit
– “12 Hours”, “3 Days”, “6 Minutes”
• Work is ongoing for both DTCS and TWCS
– CASSANDRA-9645 to make DTCS easier to use
– CASSANDRA-10276 to make DTCS do STCS within each window (patch
available)
– CASSANDRA-10280 to make DTCS work well with old data

• There’s no guarantee that TWCS will make it into the project
– TWCS is certainly easier to reason about, but DTCS was there first and is
already deployed by real users
– Anecdotal evidence and preliminary benchmarks suggest TWCS comes out
ahead based on current state of both strategies (at the time of these slides)
– Formal benchmarking is needed
– DTCS probably wins for reads/SELECTS in SOME data models
• Even if TWCS doesn’t make it in, the source is available now on (see:
CASSANDRA-9666)
– It’s likely we’ll continue to maintain it, even if it’s not accepted upstream, so
pull requests are welcome

Q&A
• Talk to me about Cassandra or DTCS on twitter: @jjirsa
• Try to stop me from talking about DTCS on IRC: #cassandra
• Crowdstrike is awesome and hiring
– www.crowdstrike.com/careers/
• Jim Plush and Dennis Opacki, tomorrow morning
– “1 Million Writes Per Second on 60 Nodes with Cassandra and EBS”

Cassandra Summit 2015: Real World DTCS For Operators

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