# ScyllaDB CQL Extensions ScyllaDB extends the CQL language to provide a few extra features. This document lists those 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. For example, one can use ``USING TIMEOUT ... and TTL ...`` to specify both a non-default timeout and a ttl. Examples: ```cql SELECT * FROM t USING TIMEOUT 200ms; ``` ```cql INSERT INTO t(a,b,c) VALUES (1,2,3) USING TIMESTAMP 42 AND TIMEOUT 50ms; ``` ```cql TRUNCATE TABLE t USING TIMEOUT 5m; ``` Working with prepared statements works as usual - the timeout parameter can be explicitly defined or provided as a marker: ```cql SELECT * FROM t USING TIMEOUT ?; ``` ```cql INSERT INTO t(a,b,c) VALUES (?,?,?) USING TIMESTAMP 42 AND TIMEOUT 50ms; ``` The timeout parameter can be applied to the following data modification queries: INSERT, UPDATE, DELETE, PRUNE MATERIALIZED VIEW, BATCH, and to the TRUNCATE data definition query. In addition, the timeout parameter can be applied to SELECT queries as well. ## 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: ```cql 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. By default, the PRUNE MATERIALIZED VIEW statement is relatively slow, only performing one base read or write at a time. This can be changed with the USING CONCURRENCY clause. If the clause is used, the concurrency of reads and writes from the base table will be allowed to increase up to the specified value. For example, to run the PRUNE with 100 parallel reads/writes, you can use: ```cql PRUNE MATERIALIZED VIEW my_view WHERE v = 19 USING CONCURRENCY 100; ``` ## 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. ScyllaDB allows 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: ```cql 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: ```cql 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: ```cql ALTER MATERIALIZED VIEW main.mv WITH synchronous_updates = false; ``` Even in an asynchronous view, _some_ view updates may be done synchronously. This happens when the materialized-view replica is on the same node as the base-table replica. This happens, for example, in tables using vnodes where the base table and the view have the same partition key; But is not the case if the table uses tablets: With tablets, the base and view tablets may migrate to different nodes. In general, users should not, and cannot, rely on these serendipitous synchronous view updates; If synchronous view updates are important, mark the view explicitly with `synchronous_updates = true`. ### Synchronous global secondary indexes Synchronous updates can also be turned on for global secondary indexes. At the time of writing this paragraph there is no direct syntax to do that, but it's possible to mark the underlying materialized view of an index as synchronous. ScyllaDB's implementation of secondary indexes is based on materialized views and the generated view's name can be extracted from schema tables, and is generally constructed by appending `_index` suffix to the index name: ```cql 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; ``` Local secondary indexes already have synchronous updates, so there's no need to explicitly mark them as such. ## Expressions ### NULL Scylla aims for a uniform handling of NULL values in expressions, inspired by SQL: The overarching principle is that a NULL signifies an _unknown value_, so most expressions calculated based on a NULL also results in a NULL. For example, the results of `x + NULL`, `x = NULL` or `x < NULL` are all NULL, no matter what `x` is. Even the expression `NULL = NULL` evaluates to NULL, not TRUE. But not all expressions of NULL evaluate to NULL. An interesting example is boolean conjunction:`FALSE AND NULL` returns FALSE - not NULL. This is because no matter which unknown value the NULL represents, ANDing it with FALSE will always result in FALSE. So the return value is not unknown - it is a FALSE. In contrast, `TRUE AND NULL` does return NULL, because if we AND a TRUE with an unknown value the result is also unknown: `TRUE AND TRUE` is TRUE but `TRUE AND FALSE` is FALSE. Because `x = NULL` always evaluates to NULL, a `SELECT` filter `WHERE x = NULL` matches no row (_matching_ means evaluating to TRUE). It does **not** match rows where x is missing. If you really want to match rows with missing x, SQL offers a different syntax `x IS NULL` (and similarly, also `x IS NOT NULL`), Scylla does not yet implement this syntax. In contrast, Cassandra is less consistent in its handling of nulls. The example `x = NULL` is considered an error, not a valid expression whose result is NULL. The rules explained above apply to most expressions, in particular to `WHERE` filters in `SELECT`. However, the evaluation rules for LWT IF clauses (_conditional updates_) are _different_: a `IF x = NULL` condition succeeds if `x` is unset. This non-standard behavior of NULLs in IF expressions may be made configurable in a future version. ## `NULL` is valid input for LWT IF clause element access The LWT IF clauses ```cql IF some_map[:var] = 3 ``` or ```cql IF some_map[:var] != 3 ``` is an error if `:var` is `NULL` on Cassandra, but is accepted by Scylla. The result of the comparison, for both `=` and `!=`, is `FALSE`. ## `NULL` is valid input for LWT IF clause `LIKE` patterns The LWT IF clauses ```cql IF some_column LIKE :pattern ``` is an error if `:pattern` is `NULL` on Cassandra, but is accepted by Scylla. The result of the pattern match is `FALSE`. For more details, see: - [Lightweight Transactions](../features/lwt.rst) - [How does ScyllaDB LWT Differ from Apache Cassandra?](../kb/lwt-differences.rst) ## 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 returning the state type. ```cql CREATE FUNCTION row_fct(acc tuple, val int) RETURNS NULL ON NULL INPUT RETURNS tuple LANGUAGE lua AS $$ return { acc[1]+val, acc[2]+1 } $$; CREATE FUNCTION reduce_fct(acc tuple, acc2 tuple) RETURNS NULL ON NULL INPUT RETURNS tuple LANGUAGE lua AS $$ return { acc[1]+acc2[1], acc[2]+acc2[2] } $$; CREATE FUNCTION final_fct(acc tuple) 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 REDUCEFUNC reduce_fct FINALFUNC final_fct INITCOND (0, 0); ``` ### Behavior of bind variables references with the same name If a bind variable is referred to twice (example: `WHERE aa = :var AND bb = :var`; `:var` is referenced twice), ScyllaDB and Cassandra treat it differently: - Cassandra ignores the double reference and treats the two as two separate variables. They can have different types, and occupy two slots in the bind variable metadata (used by drivers when the user provides a bind variable tuple rather than a map) - ScyllaDB treats the two references as referring to the same variable. The two references must have the same type, and occupy one slot in the bind variable metadata. ScyllaDB can revert to the Cassandra treatment by setting the configuration item `cql_duplicate_bind_variable_names_refer_to_same_variable` to `false`. ### Lists elements for filtering Subscripting a list in a WHERE clause is supported as are maps. ```cql 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: ```cql 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: ```cql 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](https://github.com/scylladb/scylla/blob/master/docs/dev/per-partition-rate-limit.md) - Description of the [rate limit exceeded](https://github.com/scylladb/scylla/blob/master/docs/dev/protocol-extensions.md#rate-limit-error) error ## Effective service level Actual values of service level's options may come from different service levels, not only from the one user is assigned with. To facilitate insight into which values come from which service level, there is ``LIST EFFECTIVE SERVICE LEVEL OF `` command. ```cql > LIST EFFECTIVE SERVICE LEVEL OF role2; service_level_option | effective_service_level | value ----------------------+-------------------------+------------- workload_type | sl2 | batch timeout | sl1 | 2s ``` For more details, check [Service Levels docs](https://github.com/scylladb/scylla/blob/master/docs/cql/service-levels.rst) ## DESCRIBE SCHEMA WITH INTERNALS [AND PASSWORDS] We extended the semantics of `DESCRIBE SCHEMA WITH INTERNALS`: aside from describing the elements of the schema, it also describes authentication/authorization and service levels. Additionally, we introduced a new tier of the statement: `DESCRIBE SCHEMA WITH INTERNALS AND PASSWORDS`, which also includes the information about hashed passwords of the roles. For more details, see [the article on DESCRIBE SCHEMA](./describe-schema.rst).