There are two types of time to live (TTL) in YCQL:

  • Table-level TTL that is not stored in DocDB. Instead, it is stored in the YB-Master system catalog as part of the table’s schema.
  • Non-table-level TTL that includes the column-level TTL stored with the value of the column and the row-level TTL.

If no TTL is present at the column’s value, the table-level TTL acts as the default value.

Furthermore, YCQL distinguishes between rows created using INSERT and UPDATE statements. This difference as well as the row-level TTL are tracked using a so-called liveness column, which is a special system column invisible to you. It is added for inserts, but not updates, ensuring that the row is present even if all non-primary key columns are deleted only in the case of inserts.

Table-level TTL

YCQL allows the TTL property to be specified at the table level, in which case you do not store the TTL on a per key-value basis in DocDB. Instead, the TTL is implicitly enforced on reads and during compactions to reclaim space.

The table-level TTL can be defined using the default_time_to_live property.

Row-level TTL

YCQL allows the TTL property to be specified at the level of each INSERT and UPDATE operation.

The row-level TTL expires the whole row. The value is specified during an INSERT or UPDATE operation with the USING TTL clause. In such cases, the TTL is stored as part of the DocDB value.

Consider the following query:

INSERT INTO pageviews(path) VALUES ('/index') USING TTL 10;
SELECT * FROM pageviews;
 path   | views
--------+-------
 /index |  null

(1 rows)

After 10 seconds, the row is expired, as demonstrated by the following:

SELECT * FROM pageviews;
 path | views
------+-------

(0 rows)

Column-level TTL

YCQL also allows you to set a column-level TTL, in which case the TTL is stored as part of the DocDB column value that you can set only when updating the column, as demonstrated by the following example:

INSERT INTO pageviews(path,views) VALUES ('/index', 10);

SELECT * FROM pageviews;
 path   | views
--------+-------
 /index |  10

(1 rows)
UPDATE pageviews USING TTL 10 SET views=10 WHERE path='/index';

After 10 seconds, querying for the rows will result in the views column returning NULL but the row will still exist, as per the following example:

SELECT * FROM pageviews;
 path   | views
--------+-------
 /index |  null

(1 rows)

Efficient data expiration for TTL

YCQL includes a file expiration feature optimized for workloads that primarily rely on table-level TTL settings (or have consistent row- or column-level TTL values). This feature reduces both CPU usage and space amplification, particularly for time series workloads that frequently use table-level TTL to hold a dataset to a specific size. This is accomplished by organizing data into files by time, similar to Cassandra's time window compaction strategy.

This feature is available in YugabyteDB releases 2.6.10 or later, 2.8.2 or later, and 2.12.1 or later.

Configuring for new YCQL datasets

If configuring a new YCQL database for time series datasets and using a default time to live, the following YB-TServer flag configurations are recommended:

--tablet_enable_ttl_file_filter = true

Enables expired files to be directly deleted, rather than relying on garbage collection during compaction.

--rocksdb_max_file_size_for_compaction = [the amount of data to be deleted at once, in bytes]

The value for this flag depends on how much data is expected to be deleted at once. For example, if a table's TTL is 90 days, it might be desireable to delete three days' worth of data at once. In this case, rocksdb_max_file_size_for_compaction should be set to the amount of data expected to be generated in 3 days. Files over this size will be excluded from normal compactions, leading to CPU gains.

Note that there is some tradeoff here between the number of files created and read perforance. A reasonable rule of thumb is to configure the flag such that 30 to 50 files store the full dataset (for example, 90 days divided by 3 days is 30 files). CPU benefits of using this feature should more than make up for any read performance loss.

--sst_files_soft_limit = [number of expected files at steady state + 20]
--sst_files_hard_limit = [number of expected files at steady state + 40]

The value of these flags depends on the number of files expected to hold the full dataset at steady state. These flags throttle writes to YCQL if the number of files per tablet exceed their value. Thus, they need to be set in a way that can accomodate the number of files expected in the dataset's steady state. In the example above, 30 files should hold 90 days worth of data. In this case, sst_files_soft_limit would be set to 50, and sst_files_hard_limit set to 70.

In some fresh dataset cases, new data will be backfilled into the database before the application is turned on. This backfilled data may have a value-level TTL associated with it that is significantly lower than the default_time_to_live property on the table, with the desired effect being that this data be removed earlier than the table TTL would allow. By default, such data does not expire early. However, the file_expiration_value_ttl_overrides_table_ttl flag can be used to ignore table TTL and expire solely based on value TTL.

Warning

When using the file_expiration_value_ttl_overrides_table_ttl flag, be sure to set the flag back to false before all data with value-level TTL (for example, backfilled data) has fully expired. Failing to do so can result in unexpected loss of data. For example, if the default_time_to_live is 90 days, and data has been backfilled with value-level TTL from 1 day to 89 days, it is important that the file_expiration_value_ttl_overrides_table_ttl flag be set back to false within 89 days of data ingestion to avoid data loss.

Configuring for existing YCQL datasets

To convert existing YCQL tables to ones configured for file expiration, the same TServer flag values as above can be used. However, a temporary 2 times space amplification of the data should be expected in this case. This amplification happens because the existing file structure will have kept most data in a single large file, and that file will now be excluded from compactions going forward. Thus, this file will be unchanged until its contents have entirely expired, approximately TTL amount of time after the file expiration feature was configured.

Additionally, if data files were created with YugabyteDB versions 2.6.6 or earlier, or 2.8.1 or earlier, files may lack the necessary metadata to be expired naturally. The file_expiration_ignore_value_ttl flag can be set to true to ignore the missing metadata. This will ignore the row- and column-level TTL metadata, expiring files purely based on the table's default_time_to_live.

Warning

To prevent early data deletion, it is very important that in these cases, the default_time_to_live for any tables with TTL should be set to greater than or equal to the largest value-level TTL contained within those tables. It is recommended that once the files lacking the metadata have been removed, the file_expiration_ignore_value_ttl flag be set back to false (no restart required).

Best practices and troubleshooting

  • The file expiration feature is only enabled for tables with a default time to live. Even applications that explicitly set TTL on insert should be configured with a default time to live.
  • The file expiration feature assumes that data arrives in rough chronological order relative to its expected expiration time. The feature is safe to use if this assumption is not met, but will be significantly less effective.
  • Files are expired in a conservative manner, only being deleted after every data item it holds has completely expired. If a file has both a table-level TTL and column-level TTL, the later of the two is used in determining expiration.
  • If a universe was created on a YugabyteDB version earlier than 2.6.6 or 2.8.1, files may not contain the necessary metadata for file expiration. Similarly, if a data item is inserted with an unreasonably high TTL (or no TTL), the file expiration feature will stop being able to garbage-collect data. In these cases, it may become necessary to set the file_expiration_ignore_value_ttl flag to true, which may lead to unwanted loss of data. For more information, see File expiration based on TTL flags.
  • If backfilling data into a table using a column TTL lower than the default TTL, it should be expected that this data will not expire until the table's default TTL has been exceeded. This can be circumvented by setting the file_expiration_value_ttl_overrides_table_ttl flag to true, which may lead to unwanted loss of data. For more information, see File expiration based on TTL flags.

There are several ways to work with TTL:

  1. Table-level TTL with the default_time_to_live property.
  2. Expiring rows with TTL.
  3. TTL function to return number of seconds until expiration.
  4. WriteTime function returns timestamp when a row or column was inserted.
  5. Update row or column TTL to update the TTL of a row or column.
  6. YB-TServer flags related to TTL to configure the YB-TServer for file expiration based on TTL.