MaxScale 24.08 Beta Cache
Cache
This filter was introduced in MariaDB MaxScale 2.1.
Overview
From MaxScale version 2.2.11 onwards, the cache filter is no longer considered experimental. The following changes to the default behaviour have also been made:
- The default value of
cached_data
is nowthread_specific
(used to beshared
). - The default value of
selects
is nowassume_cacheable
(used to beverify_cacheable
).
The cache filter is a simple cache that is capable of caching the result of SELECTs, so that subsequent identical SELECTs are served directly by MaxScale, without the queries being routed to any server.
By default the cache will be used and populated in the following circumstances:
- There is no explicit transaction active, that is, autocommit is used,
- there is an explicitly read-only transaction (that is,
START TRANSACTION READ ONLY
) active, or - there is a transaction active and no statement that modifies the database has been performed.
In practice, the last bullet point basically means that if a transaction has
been started with BEGIN
, START TRANSACTION
or START TRANSACTION READ
WRITE
, then the cache will be used and populated until the first UPDATE
,
INSERT
or DELETE
statement is encountered.
That is, in default mode the cache effectively causes the system to behave
as if the isolation level would be READ COMMITTED
, irrespective of what
the isolation level of the backends actually is.
The default behaviour can be altered using the configuration parameter cache_in_transactions.
By default it is assumed that all SELECT
statements are cacheable, which
means that also statements like SELECT LOCALTIME
are cached. Please check
selects for how to change the default behaviour.
Limitations
All of these limitations may be addressed in forthcoming releases.
Prepared Statements
Resultsets of prepared statements are not cached.
Multi-statements
Multi-statements are always sent to the backend and their result is not cached.
Security
The cache is not aware of grants.
The implication is that unless the cache has been explicitly configured who the caching should apply to, the presence of the cache may provide a user with access to data he should not have access to.
Please read the section Security for more detailed information.
However, from 2.5 onwards it is possible to configure the cache to cache the data of each user separately, which effectively means that there can be no unintended sharing. Please see users for how to change the default behaviour.
information_schema
When invalidation is enabled, SELECTs targeting tables
in information_schema
are not cached. The reason is that as the content
of the tables changes as the side-effect of something else, the cache would
not know when to invalidate the cache-entries.
Invalidation
Since MaxScale 2.5, the cache is capable of invalidating entries in the cache when a modification (UPDATE, INSERT or DELETE) that may affect those entries is made.
The cache invalidation works on the table-level, that is, a modification made to a particular table will cause all cache entries that refer to that table to be invalidated, irrespective of whether the modification actually has an impact on the cache entries or not. For instance, suppose the result of the following SELECT has been cached
SELECT * FROM t WHERE a=1;
An insert like
INSERT INTO t SET a=42;
will cause the cache entry containing the result of that SELECT to be invalidated even if the INSERT actually does not affect it. Please see invalidate for how to enable the invalidation.
When invalidation has been enabled MaxScale must be able to completely parse a SELECT statement for its results to be stored in the cache. The reason is that in order to be able to invalidate cache entries, MaxScale must know what tables a SELECT statement depends upon. Consequently, if (and only if) invalidation has been enabled and MaxScale fails to parse a statement, the result of that particular statement will not be cached.
When invalidation has been enabled, MaxScale will also parse all UPDATE, INSERT and DELETE statements, in order to find out what tables are modified. If that parsing fails, MaxScale will by default clear the entire cache. The reason is that unless MaxScale can completely parse the statement it cannot know what tables are modified and hence not what cache entries should be invalidated. Consequently, to prevent stale data from being returned, the entire cache is cleared. The default behaviour can be changed using the configuration parameter clear_cache_on_parse_errors.
Note that what threading approach is used has a big impact on the invalidation. Please see Threads, Users and Invalidation for how the threading approach affects the invalidation.
Note also that since the invalidation may not, depending on how the cache has been configured, be visible to all sessions of all users, it is still important to configure a reasonable soft and hard TTL.
Best Efforts
The invalidation offered by the MaxScale cache can be said to be of best efforts quality. The reason is that in order to ensure that the cache in all circumstances reflects the state in the actual database, would require that the operations involving the cache and the MariaDB server are synchronized, which would cause an unacceptable overhead.
What best efforts means in this context is best illustrated using an example.
Suppose a client executes the statement SELECT * FROM tbl
and that the result
is cached. Next time that or any other client executes the same statement, the
result is returned from the cache and the MariaDB server will not be accessed
at all.
If a client now executes the statement INSERT INTO tbl VALUES (...)
, the
cached value for the SELECT
statement above and all other statements that are
dependent upon tbl
will be invalidated. That is, the next time someone executes
the statement SELECT * FROM tbl
the result will again be fetched from the
MariaDB server and stored to the cache.
However, suppose some client executes the statement SELECT COUNT(*) FROM tbl
at the same time someone else executes the INSERT ...
statement. A possible
chain of events is as follows:
Timeline 1 Timeline 2 Clients execute INSERT ... SELECT COUNT(*) FROM tbl MaxScale -> DB SELECT COUNT(*) FROM tbl MaxScale -> DB INSERT ...
That is, the SELECT
is performed in the database server before the
INSERT
. However, since the timelines are proceeding independently of
each other, the events may be re-ordered as far as the cache is concerned.
MaxScale -> Cache Delete invalidated values MaxScale -> Cache Store result and invalidation key
That is, the cached value for SELECT COUNT(*) FROM tbl
will reflect the
situation before the insert and will thus not be correct.
The stale result will be returned until the value has reached its time-to-live or its invalidation is caused by some update operation.
Configuration
The cache is simple to add to any existing service. However, some experimentation may be required in order to find the configuration settings that provide the maximum benefit.
[Cache] type=filter module=cache hard_ttl=30 soft_ttl=20 rules=... ... [Cached-Routing-Service] type=service ... filters=Cache
Each configured cache filter uses a storage of its own. That is, if there are two services, each configured with a specific cache filter, then, even if queries target the very same servers the cached data will not be shared.
Two services can use the same cache filter, but then either the services should use the very same servers or a completely different set of servers, where the used table names are different. Otherwise there can be unintended sharing.
Filter Parameters
The cache filter has no mandatory parameters but a range of optional ones.
Note that it is advisable to specify max_size
to prevent the cache from
using up all memory there is, in case there is very little overlap among the
queries.
storage
The name of the module that provides the storage for the cache. That
module will be loaded and provided with the value of storage_options
as
argument. For instance:
storage=storage_inmemory
The default is storage_inmemory
.
See Storage for what storage modules are available.
storage_options
NOTE Deprecated in 23.02.
A string that is provided verbatim to the storage module specified in storage
,
when the module is loaded. Note that the needed arguments and their format depend
upon the specific module.
From 23.02 onwards, the storage module configuration should be provided using nested parameters.
hard_ttl
- Type: duration
- Mandatory: No
- Dynamic: No
- Default:
0s
(no limit)
Hard time to live; the maximum amount of time the cached result is used before it is discarded and the result is fetched from the backend (and cached). See also soft_ttl.
hard_ttl=60s
soft_ttl
- Type: duration
- Mandatory: No
- Dynamic: No
- Default:
0s
(no limit)
Soft time to live; the amount of time - in seconds - the cached result is
used before it is refreshed from the server. When soft_ttl
has passed, the
result will be refreshed when the first client requests the value.
However, as long as hard_ttl has not passed, all other clients
requesting the same value will use the result from the cache while it is being
fetched from the backend. That is, as long as soft_ttl
but not hard_ttl
has passed, even if several clients request the same value at the same time,
there will be just one request to the backend.
soft_ttl=60s
If the value of soft_ttl
is larger than hard_ttl
it will be adjusted
down to the same value.
max_resultset_rows
- Type: count
- Mandatory: No
- Dynamic: No
- Default:
0
(no limit)
Specifies the maximum number of rows a resultset can have in order to be stored in the cache. A resultset larger than this, will not be stored.
max_resultset_rows=1000
max_resultset_size
- Type: size
- Mandatory: No
- Dynamic: No
- Default:
0
(no limit)
Specifies the maximum size of a resultset, for it to be stored in the cache. A resultset larger than this, will not be stored. The size can be specified as described here.
max_resultset_size=128Ki
Note that the value of max_resultset_size
should not be larger than the
value of max_size
.
max_count
- Type: count
- Mandatory: No
- Dynamic: No
- Default:
0
(no limit)
The maximum number of items the cache may contain. If the limit has been reached and a new item should be stored, then an older item will be evicted.
Note that if cached_data
is thread_specific
then this limit will be
applied to each cache separately. That is, if a thread specific cache
is used, then the total number of cached items is #threads * the value
of max_count
.
max_count=1000
max_size
- Type: size
- Mandatory: No
- Dynamic: No
- Default:
0
(no limit)
The maximum size the cache may occupy. If the limit has been reached and a new item should be stored, then some older item(s) will be evicted to make space.
Note that if cached_data
is thread_specific
then this limit will be
applied to each cache separately. That is, if a thread specific cache
is used, then the total size is #threads * the value of max_size
.
max_size=100Mi
rules
- Type: path
- Mandatory: No
- Dynamic: Yes
- Default:
""
(no rules)
Specifies the path of the file where the caching rules are stored. A relative path is interpreted relative to the data directory of MariaDB MaxScale.
rules=/path/to/rules-file
Note that the rules will be reloaded, and applied if different, every time a dynamic configuration change is made. Thus, to cause a reloading of the rules, alter the rules parameter to the same value it has.
maxctrl alter filter MyCache rules='/path/to/rules-file'
cached_data
- Type: enum
- Mandatory: No
- Dynamic: No
- Values:
shared
,thread_specific
- Default:
thread_specific
An enumeration option specifying how data is shared between threads. The allowed values are:
shared
: The cached data is shared between threads. On the one hand it implies that there will be synchronization between threads, on the other hand that all threads will use data fetched by any thread.thread_specific
: The cached data is specific to a thread. On the one hand it implies that no synchronization is needed between threads, on the other hand that the very same data may be fetched and stored multiple times.
cached_data=shared
Default is thread_specific
. See max_count
and max_size
what implication
changing this setting to shared
has.
selects
- Type: enum
- Mandatory: No
- Dynamic: Yes
- Values:
assume_cacheable
,verify_cacheable
- Default:
assume_cacheable
An enumeration option specifying what approach the cache should take with
respect to SELECT
statements. The allowed values are:
assume_cacheable
: The cache can assume that allSELECT
statements, without exceptions, are cacheable.verify_cacheable
: The cache can not assume that allSELECT
statements are cacheable, but must verify that.
selects=verify_cacheable
Default is assume_cacheable
. In this case, all SELECT
statements are
assumed to be cacheable and will be parsed only if some specific rule
requires that.
If verify_cacheable
is specified, then all SELECT
statements will be
parsed and only those that are safe for caching - e.g. do not call any
non-cacheable functions or access any non-cacheable variables - will be
subject to caching.
If verify_cacheable
has been specified, the cache will not be used in
the following circumstances:
- The
SELECT
uses any of the following functions:BENCHMARK
,CONNECTION_ID
,CONVERT_TZ
,CURDATE
,CURRENT_DATE
,CURRENT_TIMESTAMP
,CURTIME
,DATABASE
,ENCRYPT
,FOUND_ROWS
,GET_LOCK
,IS_FREE_LOCK
,IS_USED_LOCK
,LAST_INSERT_ID
,LOAD_FILE
,LOCALTIME
,LOCALTIMESTAMP
,MASTER_POS_WAIT
,NOW
,RAND
,RELEASE_LOCK
,SESSION_USER
,SLEEP
,SYSDATE
,SYSTEM_USER
,UNIX_TIMESTAMP
,USER
,UUID
,UUID_SHORT
. - The
SELECT
accesses any of the following fields:CURRENT_DATE
,CURRENT_TIMESTAMP
,LOCALTIME
,LOCALTIMESTAMP
- The
SELECT
uses system or user variables.
Note that parsing all SELECT
statements carries a performance
cost. Please read performance for more details.
cache_in_transactions
- Type: enum
- Mandatory: No
- Dynamic: No
- Values:
never
,read_only_transactions
,all_transactions
- Default:
all_transactions
An enumeration option specifying how the cache should behave when there are active transactions:
never
: When there is an active transaction, no data will be returned from the cache, but all requests will always be sent to the backend. The cache will be populated inside explicitly read-only transactions. Inside transactions that are not explicitly read-only, the cache will be populated until the first non-SELECT statement.read_only_transactions
: The cache will be used and populated inside explicitly read-only transactions. Inside transactions that are not explicitly read-only, the cache will be populated, but not used until the first non-SELECT statement.all_transactions
: The cache will be used and populated inside explicitly read-only transactions. Inside transactions that are not explicitly read-only, the cache will be used and populated until the first non-SELECT statement.
cache_in_transactions=never
Default is all_transactions
.
The values read_only_transactions
and all_transactions
have roughly the
same effect as changing the isolation level of the backend to read_committed
.
debug
- Type: number
- Mandatory: No
- Dynamic: No
- Default:
0
An integer value, using which the level of debug logging made by the cache can be controlled. The value is actually a bitfield with different bits denoting different logging.
0
(0b00000
) No logging is made.1
(0b00001
) A matching rule is logged.2
(0b00010
) A non-matching rule is logged.4
(0b00100
) A decision to use data from the cache is logged.8
(0b01000
) A decision not to use data from the cache is logged.16
(0b10000
) Higher level decisions are logged.
Default is 0
. To log everything, give debug
a value of 31
.
debug=31
enabled
- Type: boolean
- Mandatory: No
- Dynamic: No
- Default:
true
Specifies whether the cache is initially enabled or disabled.
enabled=false
The value affects the initial state of the MaxScale user variables using which the behaviour of the cache can be modified at runtime. Please see Runtime Configuration for details.
invalidate
- Type: enum
- Mandatory: No
- Dynamic: No
- Values:
never
,current
- Default:
never
An enumeration option specifying how the cache should invalidate cache entries.
* `never`: No invalidation is performed. This is the default.
* `current`: When a modification is made, entries in the cache used by
the current session are invalidated. Other sessions that use the same
cache will also be affected, but sessions that use another cache will
not.
The effect of current
depends upon the value of cached_data
. If the value
is shared
, that is, all threads share the same cache, then the effect of an
invalidation is immediately visible to all sessions, as there is just one cache.
However, if the value is thread_specific
, then an invalidation will affect only
the cache that the session happens to be using.
If it is important and sufficient that an application immediately sees a change
that it itself has caused, then a combination of invalidate=current
and cached_data=thread_specific
can be used.
If it is important that an application immediately sees all changes, irrespective
of who has caused them, then a combination of invalidate=current
and cached_data=shared
must be used.
clear_cache_on_parse_errors
- Type: boolean
- Mandatory: No
- Dynamic: No
- Default:
true
This boolean option specifies how the cache should behave in case of parsing errors when invalidation has been enabled.
true
: If the cache fails to parse an UPDATE/INSERT/DELETE statement then all cached data will be cleared.false
: A failure to parse an UPDATE/INSERT/DELETE statement is ignored and no invalidation will take place due that statement.
The default value is true
.
Changing the value to false
may mean that stale data is returned from
the cache, if an UPDATE/INSERT/DELETE cannot be parsed and the statement
affects entries in the cache.
users
- Type: enum
- Mandatory: No
- Dynamic: No
- Values:
mixed
,isolated
- Default:
mixed
An enumeration option specifying how the cache should cache data for different users.
* `mixed`: The data of different users is stored in the same
cache. This is the default and may cause that a user can
access data he should not have access to.
* `isolated`: Each user has a unique cache and there can be
no unintended sharing.
Note that if isolated
has been specified, then each user will
conceptually have a cache of his own, which is populated
independently from each other. That is, if two users make the
same query, then the data will be fetched twice and also stored
twice. So, a isolated
cache will in general use more memory and
cause more traffic to the backend compared to a mixed
cache.
timeout
- Type: duration
- Mandatory: No
- Dynamic: No
- Default:
5s
The timeout used when performing operations to distributed storages such as redis or memcached.
timeout=7000ms
Runtime Configuration
The cache filter can be configured at runtime by executing SQL commands. If there is more than one cache filter in a service, only the first cache filter will be able to process the variables. The remaining filters will not see them and thus configuring them at runtime is not possible.
@maxscale.cache.populate
Using the variable @maxscale.cache.populate
it is possible to specify at
runtime whether the cache should be populated or not. Its initial value is
the value of the configuration parameter enabled
. That is, by default the
value is true
.
The purpose of this variable is make it possible for an application to decide statement by statement whether the cache should be populated.
SET @maxscale.cache.populate=true; SELECT a, b FROM tbl; SET @maxscale.cache.populate=false; SELECT a, b FROM tbl;
In the example above, the first SELECT
will always be sent to the
server and the result will be cached, provided the actual cache rules
specifies that it should be. The second SELECT
may be served from the
cache, depending on the value of @maxscale.cache.use
(and the cache
rules).
The value of @maxscale.cache.populate
can be queried
SELECT @maxscale.cache.populate;
but only after it has been explicitly set once.
@maxscale.cache.use
Using the variable @maxscale.cache.use
it is possible to specify at
runtime whether the cache should be used or not. Its initial value is
the value of the configuration parameter enabled
. That is, by default the
value is true
.
The purpose of this variable is make it possible for an application to decide statement by statement whether the cache should be used.
SET @maxscale.cache.use=true; SELECT a, b FROM tbl; SET @maxscale.cache.use=false; SELECT a, b FROM tbl;
The first SELECT
will be served from the cache, providing the rules
specify that the statement should be cached, the cache indeed contains
the result and the date is not stale (as specified by the TTL).
If the data is stale, the SELECT
will be sent to the server and
the cache entry will be updated, irrespective of the value of
@maxscale.cache.populate
.
If @maxscale.cache.use
is true
but the result is not found in the
cache, and the result is subsequently fetched from the server, the
result will not be added to the cache, unless
@maxscale.cache.populate
is also true
.
The value of @maxscale.cache.use
can be queried
SELECT @maxscale.cache.use;
but only after it has explicitly been set once.
@maxscale.cache.soft_ttl
Using the variable @maxscale.cache.soft_ttl
it is possible at runtime
to specify in seconds what soft ttl should be applied. Its initial
value is the value of the configuration parameter soft_ttl
. That is,
by default the value is 0.
The purpose of this variable is make it possible for an application to decide statement by statement what soft ttl should be applied.
set @maxscale.cache.soft_ttl=600; SELECT a, b FROM unimportant; set @maxscale.cache.soft_ttl=60; SELECT c, d FROM important;
When data is SELECT
ed from the unimportant table unimportant
, the data
will be returned from the cache provided it is no older than 10 minutes,
but when data is SELECT
ed from the important table important
, the
data will be returned from the cache provided it is no older than 1 minute.
Note that @maxscale.cache.hard_ttl
overrules @maxscale.cache.soft_ttl
in the sense that if the former is less that the latter, then soft ttl
will, when used, be adjusted down to the value of hard ttl.
The value of @maxscale.cache.soft_ttl
can be queried
SELECT @maxscale.cache.soft_ttl;
but only after it has explicitly been set once.
@maxscale.cache.hard_ttl
Using the variable @maxscale.cache.hard_ttl
it is possible at runtime
to specify in seconds what hard ttl should be applied. Its initial
value is the value of the configuration parameter hard_ttl
. That is,
by default the value is 0.
The purpose of this variable is make it possible for an application to decide statement by statement what hard ttl should be applied.
Note that as @maxscale.cache.hard_ttl
overrules @maxscale.cache.soft_ttl
,
is is important to ensure that the former is at least as large as the latter
and for best overall performance that it is larger.
set @maxscale.cache.soft_ttl=600, @maxscale.cache.hard_ttl=610; SELECT a, b FROM unimportant; set @maxscale.cache.soft_ttl=60, @maxscale.cache.hard_ttl=65; SELECT c, d FROM important;
The value of @maxscale.cache.hard_ttl
can be queried
SELECT @maxscale.cache.hard_ttl;
but only after it has explicitly been set once.
Client Driven Caching
With @maxscale.cache.populate
and @maxscale.cache.use
is it possible
to make the caching completely client driven.
Provide no rules
file, which means that all SELECT
statements are
subject to caching and that all users receive data from the cache. Set
the startup mode of the cache to disabled.
[TheCache] type=filter module=cache enabled=false
Now, in order to mark statements that should be cached, set
@maxscale.cache.populate
to true
, and perform those SELECT
s.
SET @maxscale.cache.populate=true; SELECT a, b FROM tbl1; SELECT c, d FROM tbl2; SELECT e, f FROM tbl3; SET @maxscale.cache.populate=false;
Note that those SELECT
s must return something in order for the
statement to be marked for caching.
After this, the value of @maxscale.cache.use
will decide whether
or not the cache is considered.
SET @maxscale.cache.use=true; SELECT a, b FROM tbl1; SET @maxscale.cache.use=false;
With @maxscale.cache.use
being true
, the cache is considered
and the result returned from there, if not stale. If it is stale,
the result is fetched from the server and the cached entry is updated.
By setting a very long TTL it is possible to prevent the cache from ever considering an entry to be stale and instead manually cause the cache to be updated when needed.
UPDATE tbl1 SET a = ...; SET @maxscale.cache.populate=true; SELECT a, b FROM tbl1; SET @maxscale.cache.populate=false;
Threads, Users and Invalidation
What caching approach is used and how different users are treated has a significant impact on the behaviour of the cache. In the following the implication of different combinations is explained.
cached_data/users |
mixed |
isolated |
---|---|---|
thread_specific |
No thread contention. Data/work duplicated across threads. May cause unintended sharing. | No thread contention. Data/work duplicated across threads and users. No unintended sharing. Requires the most amount of memory. |
shared |
Thread contention under high load. No duplicated data/work. May cause unintended sharing. Requires the least amount of memory. | Thread contention under high load. Data/work duplicated across users. No unintended sharing. |
Invalidation
Invalidation takes place only in the current cache, so how visible
the invalidation is, depends upon the configuration value of
cached_data
.
cached_data=thread_specific
The invalidation is visible only to the sessions that are handled by the same worker thread where the invalidation occurred. Sessions of the same or other users that are handled by different worker threads will not see the new value before the TTL causes the value to be refreshed.
cache_data=shared
The invalidation is immediately visible to all sessions of all users.
Rules
The caching rules are expressed as a JSON object or as an array of JSON objects.
There are two decisions to be made regarding the caching; in what circumstances should data be stored to the cache and in what circumstances should the data in the cache be used.
Expressed in JSON this looks as follows
{ store: [ ... ], use: [ ... ] }
or, in case an array is used, as
[ { store: [ ... ], use: [ ... ] }, { ... } ]
The store
field specifies in what circumstances data should be stored to
the cache and the use
field specifies in what circumstances the data in
the cache should be used. In both cases, the value is a JSON array containing
objects.
If an array of rule objects is specified, then, when looking for a rule that
matches, the store
field of each object are evaluated in sequential order
until a match is found. Then, the use
field of that object is used when
deciding whether data in the cache should be used.
When to Store
By default, if no rules file have been provided or if the store
field is
missing from the object, the results of all queries will be stored to the
cache, subject to max_resultset_rows
and max_resultset_size
cache filter
parameters.
By providing a store
field in the JSON object, the decision whether to
store the result of a particular query to the cache can be controlled in
a more detailed manner. The decision to cache the results of a query can
depend upon
- the database,
- the table,
- the column, or
- the query itself.
Each entry in the store
array is an object containing three fields,
{ "attribute": <string>, "op": <string> "value": <string> }
where,
- the attribute can be
database
,table
,column
orquery
, - the op can be
=
,!=
,like
orunlike
, and - the value a string.
If op is =
or !=
then value is used as a string; if it is like
or unlike
, then value is interpreted as a pcre2 regular expression.
Note though that if attribute is database
, table
or column
, then
the string is interpreted as a name, where a dot .
denotes qualification
or scoping.
The objects in the store
array are processed in order. If the result
of a comparison is true, no further processing will be made and the
result of the query in question will be stored to the cache.
If the result of the comparison is false, then the next object is processed. The process continues until the array is exhausted. If there is no match, then the result of the query is not stored to the cache.
Note that as the query itself is used as the key, although the following queries
select * from db1.tbl
and
use db1; select * from tbl
target the same table and produce the same results, they will be cached separately. The same holds for queries like
select * from tbl where a = 2 and b = 3;
and
select * from tbl where b = 3 and a = 2;
as well. Although they conceptually are identical, there will be two cache entries.
Note that if a column has been specified in a rule, then a statement will match irrespective of where that particular column appears. For instance, if a rule specifies that the result of statements referring to the column a should be cached, then the following statement will match
select a from tbl;
and so will
select b from tbl where a > 5;
Qualified Names
When using =
or !=
in the rule object in conjunction with database
,
table
and column
, the provided string is interpreted as a name, that is,
dot (.
) denotes qualification or scope.
In practice that means that if attribute is database
then value may
not contain a dot, if attribute is table
then value may contain one
dot, used for separating the database and table names respectively, and
if attribute is column
then value may contain one or two dots, used
for separating table and column names, or database, table and column names.
Note that if a qualified name is used as a value, then all parts of the
name must be available for a match. Currently Maria DB MaxScale may not
always be capable of deducing in what table a particular column is. If
that is the case, then a value like tbl.field
may not necessarily
be a match even if the field is field
and the table actually is tbl
.
Implication of the default database
If the rules concerns the database
, then only if the statement refers
to no specific database, will the default database be considered.
Regexp Matching
The string used for matching the regular expression contains as much information as there is available. For instance, in a situation like
use somedb; select fld from tbl;
the string matched against the regular expression will be somedb.tbl.fld
.
Examples
Cache all queries targeting a particular database.
{ "store": [ { "attribute": "database", "op": "=", "value": "db1" } ] }
Cache all queries not targeting a particular table
{ "store": [ { "attribute": "table", "op": "!=", "value": "tbl1" } ] }
That will exclude queries targeting table tbl1 irrespective of which database it is in. To exclude a table in a particular database, specify the table name using a qualified name.
{ "store": [ { "attribute": "table", "op": "!=", "value": "db1.tbl1" } ] }
Cache all queries containing a WHERE clause
{ "store": [ { "attribute": "query", "op": "like", "value": ".*WHERE.*" } ] }
Note that this will actually cause all queries that contain WHERE anywhere, to be cached.
When to Use
By default, if no rules file have been provided or if the use
field is
missing from the object, all users may be returned data from the cache.
By providing a use
field in the JSON object, the decision whether to use
data from the cache can be controlled in a more detailed manner. The decision
to use data from the cache can depend upon
- the user.
Each entry in the use
array is an object containing three fields,
{ "attribute": <string>, "op": <string> "value": <string> }
where,
- the attribute can be
user
, - the op can be
=
,!=
,like
orunlike
, and - the value a string.
If op is =
or !=
then value is interpreted as a MariaDB account
string, that is, %
means indicates wildcard, but if op is like
or
unlike
it is simply assumed value is a pcre2 regular expression.
For instance, the following are equivalent:
{ "attribute": "user", "op": "=", "value": "'bob'@'%'" } { "attribute": "user", "op": "like", "value": "bob@.*" }
Note that if op is =
or !=
then the usual assumptions apply,
that is, a value of bob
is equivalent with 'bob'@'%'
. If like
or unlike is used, then no assumptions apply, but the string is
used verbatim as a regular expression.
The objects in the use
array are processed in order. If the result
of a comparison is true, no further processing will be made and the
data in the cache will be used, subject to the value of ttl
.
If the result of the comparison is false, then the next object is processed. The process continues until the array is exhausted. If there is no match, then data in the cache will not be used.
Note that use
is relevant only if the query is subject to caching,
that is, if all queries are cached or if a query matches a particular
rule in the store
array.
Examples
Use data from the cache for all users except admin
(actually 'admin'@'%'
),
regardless of what host the admin
user comes from.
{ "use": [ { "attribute": "user", "op": "!=", "value": "admin" } ] }
Security
As the cache is not aware of grants, unless the cache has been explicitly
configured who the caching should apply to, the presence of the cache
may provide a user with access to data he should not have access to.
Note that the following applies only if users=mixed
has been configured.
If users=isolated
has been configured, then there can never be any
unintended sharing between users.
Suppose there is a table access
that the user alice has access to,
but the user bob does not. If bob tries to access the table, he will
get an error as reply:
MySQL [testdb]> select * from access; ERROR 1142 (42000): SELECT command denied to user 'bob'@'localhost' for table 'access'
If we now setup caching for the table, using the simplest possible rules file, bob will get access to data from the table, provided he executes a select identical with one alice has executed.
For instance, suppose the rules look as follows:
{ "store": [ { "attribute": "table", "op": "=", "value": "access" } ] }
If alice now queries the table, she will get the result, which also will be cached:
MySQL [testdb]> select * from access; +------+------+ | a | b | +------+------+ | 47 | 11 | +------+------+
If bob now executes the very same query, and the result is still in the cache, it will be returned to him.
MySQL [testdb]> select current_user(); +----------------+ | current_user() | +----------------+ | [email protected] | +----------------+ 1 row in set (0.00 sec) MySQL [testdb]> select * from access; +------+------+ | a | b | +------+------+ | 47 | 11 | +------+------+
That can be prevented, by explicitly declaring in the rules that the caching should be applied to alice only.
{ "store": [ { "attribute": "table", "op": "=", "value": "access" } ], "use": [ { "attribute": "user", "op": "=", "value": "'alice'@'%'" } ] }
With these rules in place, bob is again denied access, since queries
targeting the table access
will in his case not be served from the cache.
Storage
There are two types of storages that can be used; local and shared.
The only local storage implementation is storage_inmemory
that simply
stores the cache values in memory. The storage is not persistent and is
destroyed when MaxScale terminates. Since the storage exists in the MaxScale
process, it is very fast and provides almost always a performance benefit.
Currently there are two shared storages; storage_memcached
and
storage_redis
that are implemented using memcached
and redis respectively.
The shared storages are accessed across the network and consequently it is not self-evident that their use will provide any performance benefit. Namely, irrespective of whether the data is fetched from the cache or from the server there will be a network hop and often that network hop is, as far as the performance goes, what costs the most.
The presence of a shared cache may provide a performance benefit if the network between MaxScale and the storage server (memcached or Redis) is faster than the network between MaxScale and the database server, if the used SELECT statements are heavy (that is, take a significant amount of time) to process for the database server, or * if the presence of the cache reduces the overall load of an otherwise overloaded database server.
As a general rule a shared storage should not be used without first assessing its value using a realistic workload.
storage_inmemory
This simple storage module uses the standard memory allocator for storing the cached data.
storage=storage_inmemory
This storage module takes no arguments.
storage_memcached
This storage module uses memcached for storing the cached data.
Multiple MaxScale instances can share the same memcached server and items cached by one MaxScale instance will be used by the other. Note that all MaxScale instances should have exactly the same configuration, as otherwise there can be unintended sharing.
storage=storage_memcached
storage_memcache
has the following parameters:
server
- Type: The Memcached server address specified as
host[:port]
- Mandatory: Yes
- Dynamic: No
If no port is provided, then the default port 11211
will be used.
max_value_size
- Type: size
- Mandatory: No
- Dynamic: No
- Default: 1Mi
By default, the maximum size of a value stored to memcached is 1MiB, but that can be configured to something else, in which case this parameter should be set accordingly.
The value of max_value_size
will be used for capping max_resultset_size
,
that is, if memcached has been configured to allow larger values than 1MiB
but max_value_size
has not been set accordingly, only resultsets up to 1MiB
in size will be cached.
Example
From MaxScale 23.02 onwards, the storage configuration should be provided as nested parameters.
[Cache-Filter] type=filter module=cache storage=storage_memcached storage_memcached.server=192.168.1.31 storage_memcached.max_value_size=10M
Although deprecated in 23.02, the configuration can also be provided
using storage_options
:
storage_options="server=192.168.1.31,max_value_size=10M"
Limitations
- Invalidation is not supported.
- Configuration values given to
max_size
andmax_count
are ignored.
Security
Neither the data in the memcached server nor the traffic between MaxScale and the memcached server is encrypted. Consequently, anybody with access to the memcached server or to the network have access to the cached data.
storage_redis
This storage module uses redis for storing the cached data.
Note that Redis should be configured with no idle timeout or with a timeout that is very large. Otherwise MaxScale may have to repeatedly connect to Redis, which will hurt both the functionality and the performance.
Multiple MaxScale instances can share the same redis server and items cached by one MaxScale instance will be used by the other. Note that all MaxScale instances should have exactly the same configuration, as otherwise there can be unintended sharing.
storage=storage_redis
If storage_redis
cannot connect to the Redis server, caching will silently
be disabled and a connection attempt will be made after a timeout
interval.
If a timeout error occurs during an operation, reconnecting will be attempted
after a delay, which will be an increasing multiple of timeout
. For example,
if timeout
is the default 5 seconds, then reconnection attempts will first
be made after 10 seconds, then after 15 seconds, then 20 and so on. However,
once 60 seconds have been reached, the delay will no longer be increased but
the delay will stay at one minute. Note that each time a reconnection attempt
is made, unless the reason for the timeout has disappeared, the client will be
stalled for timeout
seconds.
storage_redis
has the following parameters:
server
- Type: The Redis server address specified as
host[:port]
- Mandatory: Yes
- Dynamic: No
If no port is provided, then the default port 6379
will be used.
username
- Type: string
- Mandatory: No
- Dynamic: No
- Default:
""
Please see authentication for more information.
password
- Type: string
- Mandatory: No
- Dynamic: No
- Default:
""
Please see authentication for more information.
ssl
- Type: boolean
- Mandatory: No
- Dynamic: No
- Default:
false
Please see ssl for more information.
ssl_cert
- Type: Path to existing readable file.
- Mandatory: No
- Dynamic: No
- Default:
""
The SSL client certificate that MaxScale should use with the Redis
server. The certificate must match the key defined in ssl_key
.
Please see ssl for more information.
ssl_key
- Type: Path to existing readable file.
- Mandatory: No
- Dynamic: No
- Default:
""
The SSL client private key MaxScale should use with the Redis server.
Please see ssl for more information.
ssl_ca
- Type: Path to existing readable file.
- Mandatory: No
- Dynamic: No
- Default:
""
The Certificate Authority (CA) certificate for the CA that signed the
certificate specified with ssl_cert
.
Please see ssl for more information.
Authentication
If password
is provided, MaxScale will authenticate against Redis when a connection
has been created. The authentication is performed using the
auth command, with only the password
as argument,
if no username
was provided in the configuration, or username
and password
as
arguments, if both were.
Note that if the authentication is in the Redis configuration file
specified using requirepass
, then only the password should be provided.
If the Redis server version is 6 or higher and the Redis ACL system is used,
then both username and password must be provided.
SSL
If ssl_key
, ssl_cert
and ssl_ca
are provided, then SSL/TLS will be used
in the communication with the Redis server, if ssl
is set to true
.
Note that the SSL/TLS support is only available in Redis from version 6 onwards and that the support is not by default built into Redis, but has to be specifically enabled at compile time as explained here.
Example
From MaxScale 23.02 onwards, the storage configuration should be provided as nested parameters.
[Cache-Filter] type=filter module=cache storage=storage_redis storage_redis.server=192.168.1.31 storage_redis.username=hello storage_redis.password=world
Although deprecated in 23.02, the configuration can also be provided
using storage_options
:
storage_options="server=192.168.1.31,username=hello,password=world"
Limitations
- There is no distinction between soft and hard ttl, but only hard ttl is used.
- Configuration values given to
max_size
andmax_count
are ignored.
Invalidation
storage_redis
supports invalidation, but the caveats documented here
are of greater significance since also the communication between the cache and the
cache storage is asynchronous and takes place over the network.
NOTE If invalidation is turned on after caching has been used (in non-invalidation mode), redis must be flushed as otherwise there will be entries in the cache that will not be affected by the invalidation.
$ redis-cli flushall
Security
The data in the redis server is not encrypted. Consequently, anybody with access to the redis server has access to the cached data.
Unless SSL has been enabled, anybody with access to the network has access to the cached data.
Example
In the following we define a cache MyCache that uses the cache storage module
storage_inmemory
and whose soft ttl is 30
seconds and whose hard ttl is
45
seconds. The cached data is shared between all threads and the maximum size
of the cached data is 50
mebibytes. The rules for the cache are in the file
cache_rules.json
.
Configuration
[MyCache] type=filter module=cache storage=storage_inmemory soft_ttl=30 hard_ttl=45 cached_data=shared max_size=50Mi rules=cache_rules.json [MyService] type=service ... filters=MyCache
cache_rules.json
The rules specify that the data of the table sbtest
should be cached.
{ "store": [ { "attribute": "table", "op": "=", "value": "sbtest" } ] }
Performance
When the cache filter was introduced, the most significant factor affecting
the performance of the cache was whether the statements needed to be parsed.
Initially, all statements were parsed in order to exclude SELECT
statements
that use non-cacheable functions, access non-cacheable variables or refer
to system or user variables. Later, the default value of the selects
parameter
was changed to assume_cacheable
, to maximize the default performance.
With the default configuration, the cache itself will not cause the statements
to be parsed. However, even with assume_cacheable
configured, a rule referring
specifically to a database, table or column will still cause the
statement to be parsed.
For instance, a simple rule like
{ "store": [ { "attribute": "database", "op": "=", "value": "db1" } ] }
cannot be fulfilled without parsing the statement.
If the rule is instead expressed using a regular expression
{ "store": [ { "attribute": "query", "op": "like", "value": "FROM db1\\..*" } ] }
then the statement will not be parsed.
However, when the query classifier cache was introduced, the parsing cost was significantly reduced and currently the cost for parsing and regular expression matching is roughly the same.
In the following is a table with numbers giving a rough picture of the relative cost of different approaches.
In the table, regexp match means that the cacheable statements were picked out using a rule like
{ "attribute": "query", "op": "unlike", "value": "FROM nomatch" }
while exact match means that the cacheable statements were picked out using a rule like
{ "attribute": "database", "op": "!=", "value": "nomatch" }
The exact match rule requires all statements to be parsed.
As the purpose of the test is to illustrate the overhead of different approaches, the rules were formulated so that all SELECT statements would match.
Note that these figures were obtained by running sysbench, MaxScale and the server in the same computer, so they are only indicative.
selects |
Rule | qps |
---|---|---|
assume_cacheable |
none | 100 |
assume_cacheable |
regexp match | 83 |
assume_cacheable |
exact match | 83 |
verify_cacheable |
none | 80 |
verify_cacheable |
regexp match | 80 |
verify_cacheable |
exact match | 80 |
For comparison, without caching, the qps is 33
.
As can be seen, due to the query classifier cache there is no difference between exact and regex based matching.
Summary
For maximum performance:
- Arrange the situation so that the default
selects=assume_cacheable
can be used, and use no rules.
Otherwise it is mostly a personal preference whether exact or regex based rules are used. However, one should always test with real data and real queries before choosing one over the other.