ScyllaDB CQL Extensions¶

BYPASS CACHE clause¶

The BYPASS CACHE clause on SELECT statements informs the database that the data being read is unlikely to be read again in the near future, and also was unlikely to have been read in the near past; therefore no attempt should be made to read it from the cache or to populate the cache with the data. This is mostly useful for range scans; these typically process large amounts of data with no temporal locality and do not benefit from the cache.

The clause is placed immediately after the optional ALLOW FILTERING clause:

SELECT ... FROM ...
WHERE ...
ALLOW FILTERING          -- optional
BYPASS CACHE

“Paxos grace seconds” per-table option¶

The paxos_grace_seconds option is used to set the amount of seconds which are used to TTL data in paxos tables when using LWT queries against the base table.

This value is intentionally decoupled from gc_grace_seconds since, in general, the base table could use completely different strategy to garbage collect entries, e.g. can set gc_grace_seconds to 0 if it doesn’t use deletions and hence doesn’t need to repair.

However, paxos tables still rely on repair to achieve consistency, and the user is required to execute repair within paxos_grace_seconds.

Default value is equal to DEFAULT_GC_GRACE_SECONDS, which is 10 days.

The option can be specified at CREATE TABLE or ALTER TABLE queries in the same way as other options by using WITH clause:

CREATE TABLE tbl ...
WITH paxos_grace_seconds=1234

USING TIMEOUT¶

TIMEOUT extension allows specifying per-query timeouts. This parameter accepts a single duration and applies it as a timeout specific to a single particular query. The parameter is supported for prepared statements as well. The parameter acts as part of the USING clause, and thus can be combined with other parameters - like timestamps and time-to-live. In order for this parameter to be effective for read operations as well, it’s possible to attach USING clause to SELECT statements.

Examples:

	SELECT * FROM t USING TIMEOUT 200ms;

	INSERT INTO t(a,b,c) VALUES (1,2,3) USING TIMESTAMP 42 AND TIMEOUT 50ms;

Working with prepared statements works as usual - the timeout parameter can be explicitly defined or provided as a marker:

	SELECT * FROM t USING TIMEOUT ?;

	INSERT INTO t(a,b,c) VALUES (?,?,?) USING TIMESTAMP 42 AND TIMEOUT 50ms;

Keyspace storage options¶

Storage options allows specifying the storage format assigned to a keyspace. The default storage format is LOCAL, which simply means storing all the sstables in a local directory. Experimental support for S3 storage format is also added. This option is not fully implemented yet, but it will allow storing sstables in a shared, S3-compatible object store.

Storage options can be specified via CREATE KEYSPACE or ALTER KEYSPACE statement and it’s formatted as a map of options - similarly to how replication strategy is handled.

Examples:

CREATE KEYSPACE ks
    WITH REPLICATION = { 'class' : 'SimpleStrategy', 'replication_factor' : 3 }
    AND STORAGE = { 'type' : 'S3', 'bucket' : '/tmp/b1', 'endpoint' : 'localhost' } ;

ALTER KEYSPACE ks WITH REPLICATION = { 'class' : 'SimpleStrategy', 'replication_factor' : 3 }
    AND STORAGE = { 'type' : 'S3', 'bucket': '/tmp/b2', 'endpoint' : 'localhost' } ;

Storage options can be inspected by checking the new system schema table: system_schema.scylla_keyspaces:

    cassandra@cqlsh> select * from system_schema.scylla_keyspaces;
    
     keyspace_name | storage_options                                | storage_type
    ---------------+------------------------------------------------+--------------
               ksx | {'bucket': '/tmp/xx', 'endpoint': 'localhost'} |           S3

PRUNE MATERIALIZED VIEW statements¶

A special statement is dedicated for pruning ghost rows from materialized views. Ghost row is an inconsistency issue which manifests itself by having rows in a materialized view which do not correspond to any base table rows. Such inconsistencies should be prevented altogether and ScyllaDB is striving to avoid them, but if they happen, this statement can be used to restore a materialized view to a fully consistent state without rebuilding it from scratch.

Example usages:

  PRUNE MATERIALIZED VIEW my_view;
  PRUNE MATERIALIZED VIEW my_view WHERE token(v) > 7 AND token(v) < 1535250;
  PRUNE MATERIALIZED VIEW my_view WHERE v = 19;

The statement works by fetching requested rows from a materialized view and then trying to fetch their corresponding rows from the base table. If it turns out that the base row does not exist, the row is considered a ghost row and is thus deleted. The statement implicitly works with consistency level ALL when fetching from the base table to avoid false positives. As the example shows, a materialized view can be pruned in one go, but one can also specify specific primary keys or token ranges, which is recommended in order to make the operation less heavyweight and allow for running multiple parallel pruning statements for non-overlapping token ranges.

Synchronous materialized views¶

Usually, when a table with materialized views is updated, the update to the views happens asynchronously, i.e., in the background. This means that the user cannot know when the view updates have all finished - or even be sure that they succeeded.

However, there are circumstances where ScyllaDB does view updates synchronously - i.e., the user’s write returns only after the views were updated. This happens when the materialized-view replica is on the same node as the base-table replica. For example, if the base table and the view have the same partition key. Note that only ScyllaDB guarantees synchronous view updates in this case - they are asynchronous in Cassandra.

ScyllaDB also allows explicitly marking a view as synchronous. When a view is marked synchronous, base-table updates will wait for that view to be updated before returning. A base table may have multiple views marked synchronous, and will wait for all of them. The consistency level of a write applies to synchronous views as well as to the base table: For example, writing with QUORUM consistency level returns only after a quorum of the base-table replicas were updated and also a quorum of each synchronous view table was also updated.

Synchronous views tend to reduce the observed availability of the base table, because a base-table write would only succeed if enough synchronous view updates also succeed. On the other hand, failed view updates would be detected immediately, and appropriate action can be taken, such as retrying the write or pruning the materialized view (as mentioned in the previous section). This can improve the consistency of the base table with its views.

To create a new materialized view with synchronous updates, use:

CREATE MATERIALIZED VIEW main.mv
  AS SELECT * FROM main.t
  wHERE v IS NOT NULL
  PRIMARY KEY (v, id)
  WITH synchronous_updates = true;

To make an existing materialized view synchronous, use:

ALTER MATERIALIZED VIEW main.mv WITH synchronous_updates = true;

To return a materialized view to the default behavior (which, as explained above, usually means asynchronous updates), use:

ALTER MATERIALIZED VIEW main.mv WITH synchronous_updates = false;

create table main.t(id int primary key, v int);
create index on main.t(v);

select * from system_schema.indexes ;

 keyspace_name | table_name | index_name | kind       | options
---------------+------------+------------+------------+-----------------
          main |          t |    t_v_idx | COMPOSITES | {'target': 'v'}

(1 rows)


select keyspace_name, view_name from system_schema.views ;

 keyspace_name | view_name
---------------+---------------
          main | t_v_idx_index

(1 rows)

alter materialized view t_v_idx_index with synchronous_updates = true;

Expressions¶

REDUCEFUNC for UDA¶

REDUCEFUNC extension adds optional reduction function to user-defined aggregate. This allows to speed up aggregation query execution by distributing the calculations to other nodes and reducing partial results into final one. Specification of this function is it has to be scalar function with two arguments, both of the same type as UDA’s state, also returing the state type.

CREATE FUNCTION row_fct(acc tuple<bigint, int>, val int)
RETURNS NULL ON NULL INPUT
RETURNS tuple<bigint, int>
LANGUAGE lua
AS $$
  return { acc[1]+val, acc[2]+1 }
$$;

CREATE FUNCTION reduce_fct(acc tuple<bigint, int>, acc2 tuple<bigint, int>)
RETURNS NULL ON NULL INPUT
RETURNS tuple<bigint, int>
LANGUAGE lua
AS $$
  return { acc[1]+acc2[1], acc[2]+acc2[2] }
$$;

CREATE FUNCTION final_fct(acc tuple<bigint, int>)
RETURNS NULL ON NULL INPUT
RETURNS double
LANGUAGE lua
AS $$
  return acc[1]/acc[2]
$$;

CREATE AGGREGATE custom_avg(int)
SFUNC row_fct
STYPE tuple<bigint, int>
REDUCEFUNC reduce_fct
FINALFUNC final_fct
INITCOND (0, 0);

WHERE some_list[:index] = :value

Per-partition rate limit¶

The per_partition_rate_limit option can be used to limit the allowed rate of requests to each partition in a given table. When the cluster detects that the rate of requests exceeds configured limit, the cluster will start rejecting some of them in order to bring the throughput back to the configured limit. Rejected requests are less costly which can help reduce overload.

NOTE: Due to ScyllaDB’s distributed nature, tracking per-partition request rates is not perfect and the actual rate of accepted requests may be higher up to a factor of keyspace’s RF. This feature should not be used to enforce precise limits but rather serve as an overload protection feature.

_NOTE): This feature works best when shard-aware drivers are used (rejected requests have the least cost).

Limits are configured separately for reads and writes. Some examples:

    ALTER TABLE t WITH per_partition_rate_limit = {
        'max_reads_per_second': 100,
        'max_writes_per_second': 200
    };

Limit reads only, no limit for writes:

    ALTER TABLE t WITH per_partition_rate_limit = {
        'max_reads_per_second': 200
    };

Rejected requests receive the scylla-specific “Rate limit exceeded” error. If the driver doesn’t support it, Config_error will be sent instead.

For more details, see:

• Detailed design notes

• Description of the rate limit exceeded error