Choose a Compaction Strategy¶

Size-tiered compaction benefits¶

This is a popular strategy for LSM workloads. It results in a low and logarithmic (in size of data) number of SSTables, and the same data is copied during compaction a fairly low number of times. Use the table in Which strategy is best to determine if this is the right strategy for your needs.

Size-tiered compaction disadvantages¶

This strategy has the following drawbacks (particularly with writes):

• Continuously modifying existing rows results in each row being split across several SSTables, making reads slow, which doesn’t happen in Leveled compaction.

• Obsolete data (overwritten or deleted columns) in a very large SSTable remains, wasting space, for a long time, until it is finally merged. In overwrite-intensive loads for example, the overhead can be as much as 400%, as data will be duplicated 4X within a tier. On the other hand, the output SSTable will be the size of a single input SSTable. As a result, you will need 5X the amount of space (4 input SSTables plus one output SSTable), so 400% over the amount of data currently being stored. The allocated space will have to be checked and evaluated as your data set increases in size.

• Compaction requires a lot of temporary space as the new larger SSTable is written before the duplicates are purged. In the worst case up to half the disk space needs to be empty to allow this to happen.

To implement this strategy

Set the parameters for Size-tiered compaction.

Leveled Compaction benefits¶

With the leveled compaction strategy, the following benefits are noteworthy:

• SSTable reads are efficient. The great number of small SSTables doesn’t mean we need to look up a key in that many SSTables, because we know the SSTables in each level have disjoint ranges, so we only need to look in one SSTable in each level. In the typical case, only one SSTable needs to be read.

• The other factors making this compaction strategy efficient are that at most 10% of space will be wasted by obsolete rows, and only enough space for ~10x the small SSTable size needs to be reserved for temporary use by compaction.

Use the table in Which strategy is best to determine if this is the right strategy for your needs.

Leveled Compaction disadvantages¶

The downside of this method is there is two times more I/O on writes, so it is not as good for workloads which focus on writing mostly new data.

Only one compaction operation on the same table can run at a time, so compaction may be postponed if there is a compaction already in progress. As the size of the files is not too large, this is not really an issue.

To implement this strategy

Set the parameters for Leveled Compaction.

Incremental Compaction Strategy benefits¶

• Greatly reduces the temporary space amplification which is typical of STCS, resulting in more disk space being available for storing user data.

• The space requirement for a major compaction with ICS is almost non-existent given that the operation can release fragments at roughly same rate it produces new ones.

If you look at the following screenshot the green line shows how disk usage behaves under ICS when major compaction is issued.

Incremental Compaction Strategy disadvantages¶

• Since ICS principles of operation are the same as STCS, its disadvantages are similar to STCS’s, except for the temporary space amplification issue.

Namely:

• Continuously modifying existing rows results in each row being split across several SSTables, making reads slow, which doesn’t happen in Leveled compaction.

• Obsolete data (overwritten or deleted columns) may accumulate across tiers, wasting space, for a long time, until it is finally merged. This can be mitigated by running major compaction from time to time.

To implement this strategy

Set the parameters for Incremental Compaction.

For more information, see the Compaction KB Article.

Time-window Compaction benefits¶

• Keeps entries according to a time range, making searches for data within a given range easy to do, resulting in better read performance

• Improves over DTCS in that it reduces the number to huge compactions

• Allows you to expire an entire SSTable at once (using a TTL) as the data is already organized within a time frame

Time-window Compaction deficits¶

• Time-window compaction is only ideal for time-series workloads

To implement this strategy

Set the parameters for Time-window Compaction.

Use the table in Which strategy is best to determine if this is the right strategy for your needs.

Compaction Strategy Matrix¶

The table presents which workload works best with which compaction strategy. In cases where you have the ability to use either STCS or ICS, always choose ICS.

The comments below describe the type of amplification each compaction strategy create on each use case, using the following abbreviations:

• SA - Size Amplification

• WA - Write Amplification

• RA - Read Amplification

¹ When using Size-tiered with write-only loads it will use approximately 2x peak space - SA with Incremental, the SA is much less

² When using Leveled Compaction with write only loads you will experience high Write Amplification - WA

³ When using Size-tired or Incremental with Overwrite loads, SA occurs

⁴ When using Leveled Compaction with overwrite loads, WA occurs

⁵ When using Size-tiered with mostly read loads with little updates, SA and RA occurs

⁶ When using Leveled with mostly read loads with many updates, WA occurs in excess

⁷ When using Size-tiered or Incremental with Time Series workloads, SA, RA, and WA occurs.

⁸ When using Leveled with Time Series workloads, SA and WA occurs.

References¶

• Compaction Overview - contains in depth information on all of the strategies

• Compaction CQL Reference - covers the CQL parameters used for implementing compaction

• Scylla Summit Tech Talk: How to Ruin Performance by Choosing the Wrong Compaction Strategy