Creating a local distributed table

A distributed table in Manticore Search doesn’t hold any data. Instead it acts as a ‘master node’ to proxy the demanded query to other tables and provide merged results from the responses it receives from the ‘node’ tables. A distributed table can connect to local tables or tables located on other servers. The simplest example of a distributed table looks so:

  • Configuration file
  • RT mode
  • PHP
  • Python
  • javascript
  • Java

Configuration file RT mode PHP Python javascript Java

  1. table index_dist {
  2.   type  = distributed
  3.   local = index1
  4.   local = index2
  5.   ...
  6.  }
  1. CREATE TABLE local_dist type='distributed' local='index1' local='index2';
  1. $params = [
  2.     'body' => [
  3.         'settings' => [
  4.             'type' => 'distributed',
  5.             'local' => [
  6.                 'index1',
  7.                 'index2'
  8.             ]
  9.         ]
  10.     ],
  11.     'index' => 'products'
  12. ];
  13. $index = new \Manticoresearch\Index($client);
  14. $index->create($params);
  1. utilsApi.sql('CREATE TABLE local_dist type=\'distributed\' local=\'index1\' local=\'index2\'')
  1. res = await utilsApi.sql('CREATE TABLE local_dist type=\'distributed\' local=\'index1\' local=\'index2\'');
  1. utilsApi.sql("CREATE TABLE local_dist type='distributed' local='index1' local='index2'");

Remote tables

Remote tables are represented by the prefix agent in the definition of a distributed table. Any number of locals and agents may be combined in a distributed table. If no locals provided, it will be purely remote table, which serves just as a proxy. For example, you may have a frontend Manticore instance which listens on a number of ports and serves different protocols, and then just redirects queries to backends that accept connections only via Manticore’s internal binary protocol with, perhaps, persistent connections to avoid a connection establishing overhead. Despite the fact, that such distributed table doesn’t serve local tables itself, it still consumes machine resources, since it still needs to make final calculations such as merging results and calculating final aggregated values.

agent

  1. agent = address1 [ | address2 [...] ][:table-list]
  2. agent = address1[:table-list [ | address2[:table-list [...] ] ] ]

agent directive declares remote agents that are searched every time when the enclosing distributed table is searched. The agents are, essentially, pointers to networked tables. The value specifies address, and also can additionally specify multiple alternatives (agent mirrors) for either the address only, or the address and table list:

In both cases the address specification must be one of the following:

  1. address = hostname[:port] # eg. server2:9312
  2. address = /absolute/unix/socket/path # eg. /var/run/manticore2.sock

hostname is the remote host name, port is the remote TCP port number, table-list is a comma-separated list of table names, and square brackets [] designate an optional clause.

When table name is omitted, it is assumed the same table as the one where this line is defined. I.e. when defining agents for distributed table ‘mycoolindex’ you can just point the address, and it is assumed to query ‘mycoolindex’ table on agent’s endpoints.

When port number is omitted, it is assumed that it is 9312. However when it’s defined, but invalid (say, port 70000), it will skip such agent.

In other words, you can point every single agent to one or more remote tables, residing on one or more networked servers. There are absolutely no restrictions on the pointers. To point out a couple important things, the host can be localhost, and the remote table can be a distributed table in turn, all that is legal. That enables a bunch of very different usage modes:

  • sharding over multiple agent servers, and creating an arbitrary cluster topology
  • sharding over multiple agent servers, mirrored for HA/LB (High Availability and Load Balancing) purposes
  • sharding within localhost, to utilize multiple cores (however, it is simpler just to use multiple local tables)

All agents are searched in parallel. Index list is passed verbatim to the remote agent. How exactly that list is searched within the agent (ie. sequentially or in parallel too) depends solely on the agent configuration (see threads setting). The master has no remote control over that.

Note, agent internally executes a query ignoring option LIMIT, since each agent can have different tables and it is a client responsibility to apply the limit to the final result set. That’s the reason why the query to a physical table differs from the query to a distributed table when viewing them in the query logs. It can’t be just a full copy of the original query in order to provide correct results in such a case:

  • We make SELECT ... LIMIT 10, 10
  • We have 2 agents
  • Second agent has just 10 documents

If we just broadcast the original LIMIT 10, 10 query it will receive 0 documents from the 2nd agent, but LIMIT 10,10 should return documents 10-20 from the resulting set as you may not care about each agent in particular. That’s why we need to send the query to the agents with broader limits and the upper bound in this case is max_matches.

For example, imagine we have table dist which refers to remote table user.

Then if client sends this query:

  1. SELECT * FROM dist LIMIT 10,10;

the query will be converted to:

  1. SELECT * FROM user LIMIT 0,1000

and sent to the remote table user. 1000 here is the default max_matches. Once the distributed table receives the result it will apply LIMIT 10,10 to it and return the requested 10 documents.

The value can additionally enumerate per agent options such as:

  • ha_strategy - random, roundrobin, nodeads, noerrors (replaces table-wide ha_strategy for particular agent)
  • conn - pconn, persistent (same as agent_persistent on table-wide declaration)
  • blackhole 0,1 (same as agent_blackhole agent declaration)
  • retry_count - integer (same as agent_retry_count , but the provided value will not be multiplied to the number of mirrors)
  1. agent = address1:table-list[[ha_strategy=value, conn=value, blackhole=value]]

Example:

  1. # config on box1
  2. # sharding a table over 3 servers
  3. agent = box2:9312:shard1
  4. agent = box3:9312:shard2
  6. # config on box2
  7. # sharding a table over 3 servers
  8. agent = box1:9312:shard2
  9. agent = box3:9312:shard3
  11. # config on box3
  12. # sharding a table over 3 servers
  13. agent = box1:9312:shard1
  14. agent = box2:9312:shard3
  16. # per agent options
  17. agent = box1:9312:shard1[ha_strategy=nodeads]
  18. agent = box2:9312:shard2[conn=pconn]
  19. agent = box2:9312:shard2[conn=pconn,ha_strategy=nodeads]
  20. agent = test:9312:any[blackhole=1]
  21. agent = test:9312|box2:9312|box3:9312:any2[retry_count=2]
  22. agent = test:9312|box2:9312:any2[retry_count=2,conn=pconn,ha_strategy=noerrors]

agent_persistent

  1. agent_persistent = remotebox:9312:index2

agent_persistent asks to persistently connect to agent (i.e. don’t drop connection after query). This directive syntax matches that of the agent directive. The only difference is that the master will not open a new connection to the agent for every query and then close it. Rather, it will keep a connection open and attempt to reuse for the subsequent queries. The maximal number of such persistent connections per one agent host is limited by persistent_connections_limit option of searchd section.

Note, that you have to set the last one in something greater than 0 if you want to use persistent agent connections. Otherwise - when persistent_connections_limit is not defined, it assumes the zero num of persistent connections, and agent_persistent acts exactly as simple agent.

Persistent master-agent connections reduce TCP port pressure, and save on connection handshakes.

agent_blackhole

  1. agent_blackhole = testbox:9312:testindex1,testindex2

agent_blackhole lets you fire-and-forget queries to remote agents. That is useful for debugging (or just testing) production clusters: you can setup a separate debugging/testing searchd instance, and forward the requests to this instance from your production master (aggregator) instance without interfering with production work. Master searchd will attempt to connect and query blackhole agent normally, but it will neither wait nor process any responses. Also, all network errors on blackhole agents will be ignored. The value format is completely identical to regular agent directive.

agent_connect_timeout

  1. agent_connect_timeout = 300

agent_connect_timeout defines remote agent connection timeout. By default the value is assumed to be in milliseconds, but can have another suffix). Optional, default is 1000 (ie. 1 second).

When connecting to remote agents, searchd will wait at most this much time to complete successfully. If the timeout is reached but the connection has not been established and retries are enabled, retry will be initiated.

agent_query_timeout

  1. agent_query_timeout = 10000 # our query can be long, allow up to 10 sec

agent_query_timeout defines remote agent query timeout. By default the value is assumed to be in milliseconds, but can be suffixed). Optional, default is 3000 (ie. 3 seconds).

After connection, searchd will wait at most this much time for remote queries to complete. This timeout is fully separate from connection timeout; so the maximum possible delay caused by a remote agent equals to the sum of agent_connection_timeout and agent_query_timeout. Queries will not be retried if this timeout is reached; a warning will be produced instead.

agent_retry_count

Integer agent_retry_count specifies how many times Manticore will try to connect and query remote agents in a distributed table before reporting a fatal query error. It works the same way as agent_retry_count in section “searchd” of the configuration file, but defines the value for a particular table.

mirror_retry_count

mirror_retry_count is the same as agent_retry_count. If both values provided, mirror_retry_count will be taken, and the warning about it will be fired.

Instance-wide options

These options manage overall behaviour regarding remote agents. They are to be specified in searchd section of the configuration file and define defaults for the whole Manticore instance.

  • agent_connect_timeout - instance-wide defaults for agent_connect_timeout parameter.
  • agent_query_timeout - instance-wide defaults for agent_query_timeout parameter. The last defined in distributed (network) tables, or also may be overridden per-query using a setting of the same name.
  • agent_retry_count integer, specifies how many times manticore will try to connect and query remote agents in a distributed table before reporting fatal query error. Default is 0 (i.e. no retries). This value may be also specified on per-query basis using ‘OPTION retry_count=XXX’ clause. If per-query option exists, it will override the one specified in config.

Note, that if you use agent mirrors in definition of your distributed table, then before every attempt of connect server will select different mirror, according to specified ha_strategy specified. In this case agent_retry_count will be aggregated for all mirrors in a set.

For example, if you have 10 mirrors, and set agent_retry_count=5, then server will retry up to 50 times, assuming average 5 tries per every of 10 mirrors (in case of option ha_strategy = roundrobin it will be actually exactly 5 times per mirror).

At the same time value provided as retry_count option of agent definition serves as absolute limit. Other words, [retry_count=2] option in agent definition means always at most 2 tries, no mean if you have 1 or 10 mirrors in a line.

agent_retry_delay

agent_retry_delay integer, specifies the delay manticore takes before retrying to query a remote agent, if it fails. The value is in in milliseconds (or may include time suffix). The value makes sense only if non-zero agent_retry_count or non-zero per-query OPTION retry_count specified. Default is 500 (half a second). This value may be also specified on per-query basis using OPTION retry_delay=XXX clause. If per-query option exists, it will override the one specified in config.

client_timeout

client_timeout defines maximum time to wait between requests when using persistent connections. It is in seconds, or may be written with time suffix. The default is 5 minutes.

Example:

  1. client_timeout = 1h

hostname_lookup

hostname_lookup defines hostnames renew strategy. By default, IP addresses of agent host names are cached at server start to avoid extra flood to DNS. In some cases the IP can change dynamically (e.g. cloud hosting) and it might be desired to don’t cache the IPs. Setting this option to ‘request’ disabled the caching and queries the DNS at each query. The IP addresses can also be manually renewed with FLUSH HOSTNAMES command.

listen_tfo

listen_tfo allows TCP_FASTOPEN flag for all listeners. By default it is managed by system, but may be explicitly switched off by setting to ‘0’.

For general knowledge about TCP Fast Open extension please consult with Wikipedia. Shortly speaking, it allows to eliminate one TCP round-trip when establishing connection.

In practice using TFO in many situation may optimize client-agent network efficiency as if agent_persistent are in play, but without holding active connections, and also without limitation for the maximum num of connections.

On modern OS TFO support usually switched ‘on’ on the system level, but this is just ‘capability’, not the rule. Linux (as most progressive) supports it since 2011, on kernels starting from 3.7 (for server side). Windows supports it from some build of Windows 10. Another (FreeBSD, MacOS) also in game.

For Linux system server checks variable /proc/sys/net/ipv4/tcp_fastopen and behaves according to it. Bit 0 manages client side, bit 1 rules listeners. By default system has this param set to 1, i.e. clients enabled, listeners disabled.

persistent_connections_limit

  1. persistent_connections_limit = 29 # assume that each host of agents has max_connections = 30 (or 29).

persistent_connections_limit defines maximum # of simultaneous persistent connections to remote persistent agents. This is instance-wide option and has to be defined in searchd config section. Each time connecting an agent defined under agent_persistent we try to reuse existing connection (if any), or connect and save the connection for the future. However in some cases it makes sense to limit # of such persistent connections. This directive defines the number. It affects the number of connections to each agent’s host across all distributed tables.

It is reasonable to set the value equal or less than max_connections option of the agent’s config.

Distributed snippets creation

One special case of a distributed table is a single local + multiple remotes. It is solely used for distributed snippets creation, when snippets are sourced from files. In this case the local may be a ‘template’ table, since it is used just to provide settings for tokenization when building snippets.

snippets_file_prefix

  1. snippets_file_prefix = /mnt/common/server1/

snippets_file_prefix is a prefix to prepend to the local file names when generating snippets. Optional, default is current working folder.

Head about CALL SNIPPETS to learn more about distributed snippets creation.

Distributed percolate tables (DPQ tables)

You can construct a distributed table from several percolate tables. The syntax is absolutely the same as for other distributed tables. It can include several local tables as well as several agents.

For DPQ the operations of listing stored queries and searching through them (CALL PQ) are transparent and works as if all the tables were one solid local table. However data manipulation statements such as insert, replace, truncate are not available.

If you mention a non-percolate table among the agents, the behaviour will be undefined. Most likely in case if the erroneous agent has the same schema as the outer schema of the pq table (id, query, tags, filters) - it will not trigger an error when listing stored PQ rules hence may pollute the list of actual PQ rules stored in PQ tables with it’s own non-pq strings, so be aware of the confusion! CALL PQ to such wrong agent will definitely trigger an error.

Read more about making queries to a distribute percolate table.

Listing tables

Manticore Search has a single level of hierarchy of tables.

There is no concept of grouping tables in databases like in other DBMS. Still, Manticore accepts SHOW DATABASES statements for interoperability with SQL dialect, but the statement doesn’t return anything.

SHOW TABLES

General syntax:

  1. SHOW TABLES [ LIKE pattern ]

SHOW TABLES statement enumerates all currently active tables along with their types. Existing table types are local, distributed, rt, percolate and template.

  • SQL
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  • Python
  • javascript
  • Java

SQL PHP Python javascript Java

  1. SHOW TABLES;
  1. $client->nodes()->table();
  1. utilsApi.sql('SHOW TABLES')
  1. res = await utilsApi.sql('SHOW TABLES');
  1. utilsApi.sql("SHOW TABLES")

Response

  1. +----------+-------------+
  2. | Index    | Type        |
  3. +----------+-------------+
  4. | dist     | distributed |
  5. | plain    | local       |
  6. | pq       | percolate   |
  7. | rt       | rt          |
  8. | template | template    |
  9. +----------+-------------+
  10. 5 rows in set (0.00 sec)
  1. Array
  2. (
  3.     [dist1] => distributed
  4.     [rt] => rt
  5.     [products] => rt
  6. )
  1. {u'columns': [{u'Index': {u'type': u'string'}},
  2.               {u'Type': {u'type': u'string'}}],
  3.  u'data': [{u'Index': u'dist1', u'Type': u'distributed'},
  4.            {u'Index': u'rt', u'Type': u'rt'},
  5.            {u'Index': u'products', u'Type': u'rt'}],
  6.  u'error': u'',
  7.  u'total': 0,
  8.  u'warning': u''}
  1. {"columns":[{"Index":{"type":"string"}},{"Type":{"type":"string"}}],"data":[{"Index":"products","Type":"rt"}],"total":0,"error":"","warning":""}
  1. {columns=[{Index={type=string}}, {Type={type=string}}], data=[{Index=products, Type=rt}], total=0, error=, warning=}

Optional LIKE clause is supported for filtering tables by name.

  • SQL
  • PHP
  • Python
  • javascript
  • Java

SQL PHP Python javascript Java

  1. SHOW TABLES LIKE 'pro%';
  1. $client->nodes()->table(['body'=>['pattern'=>'pro%']]);
  1. res = await utilsApi.sql('SHOW TABLES LIKE \'pro%\'');
  1. utilsApi.sql('SHOW TABLES LIKE \'pro%\'')
  1. utilsApi.sql("SHOW TABLES LIKE 'pro%'")

Response

  1. +----------+-------------+
  2. | Index    | Type        |
  3. +----------+-------------+
  4. | products | distributed |
  5. +----------+-------------+
  6. 1 row in set (0.00 sec)
  1. Array
  2. (
  3.     [products] => distributed
  4. )
  1. {u'columns': [{u'Index': {u'type': u'string'}},
  2.               {u'Type': {u'type': u'string'}}],
  3.  u'data': [{u'Index': u'products', u'Type': u'rt'}],
  4.  u'error': u'',
  5.  u'total': 0,
  6.  u'warning': u''}
  1. {"columns":[{"Index":{"type":"string"}},{"Type":{"type":"string"}}],"data":[{"Index":"products","Type":"rt"}],"total":0,"error":"","warning":""}
  1. {columns=[{Index={type=string}}, {Type={type=string}}], data=[{Index=products, Type=rt}], total=0, error=, warning=}

DESCRIBE

  1. {DESC | DESCRIBE} table [ LIKE pattern ]

DESCRIBE statement lists table columns and their associated types. Columns are document ID, full-text fields, and attributes. The order matches that in which fields and attributes are expected by INSERT and REPLACE statements. Column types are field, integer, timestamp, ordinal, bool, float, bigint, string, and mva. ID column will be typed as bigint. Example:

  1. mysql> DESC rt;
  2. +---------+---------+
  3. | Field   | Type    |
  4. +---------+---------+
  5. | id      | bigint  |
  6. | title   | field   |
  7. | content | field   |
  8. | gid     | integer |
  9. +---------+---------+
  10. 4 rows in set (0.00 sec)

An optional LIKE clause is supported. Refer to SHOW META for its syntax details.

SELECT FROM name.table

You can also see table schema by executing the query select * from <table_name>.table. The benefit of this method is that you can use WHERE for filtering:

  • SQL

SQL

  1. select * from tbl.table where type='text';

Response

  1. +------+-------+------+----------------+
  2. | id   | field | type | properties     |
  3. +------+-------+------+----------------+
  4. |    2 | title | text | indexed stored |
  5. +------+-------+------+----------------+
  6. 1 row in set (0.00 sec)

You can also do many other things, consider <your_table_name>.table just a regular Manticore table where the columns are integer and string attributes.

  • SQL

SQL

  1. select field from tbl.table;
  2. select field, properties from tbl.table where type in ('text', 'uint');
  3. select * from tbl.table where properties any ('stored');

SHOW CREATE TABLE

  1. SHOW CREATE TABLE name

Prints the CREATE TABLE statement that creates the named table.

  • SQL

SQL

  1. SHOW CREATE TABLE tbl\G

Response

  1.        Table: tbl
  2. Create Table: CREATE TABLE tbl (
  3. f text indexed stored
  4. ) charset_table='non_cjk,cjk' morphology='icu_chinese'
  5. 1 row in set (0.00 sec)

Percolate table schemas

If you apply DESC statement to a percolate table it will show the outer table schema, i.e. the schema of stored queries. It’s static and the same for all local percolate tables:

  1. mysql> DESC pq;
  2. +---------+--------+
  3. | Field   | Type   |
  4. +---------+--------+
  5. | id      | bigint |
  6. | query   | string |
  7. | tags    | string |
  8. | filters | string |
  9. +---------+--------+
  10. 4 rows in set (0.00 sec)

If you’re looking for an expected document schema use DESC <pq table name> table:

  1. mysql> DESC pq TABLE;
  2. +-------+--------+
  3. | Field | Type   |
  4. +-------+--------+
  5. | id    | bigint |
  6. | title | text   |
  7. | gid   | uint   |
  8. +-------+--------+
  9. 3 rows in set (0.00 sec)

Also desc pq table like ... is supported and works as follows:

  1. mysql> desc pq table like '%title%';
  2. +-------+------+----------------+
  3. | Field | Type | Properties     |
  4. +-------+------+----------------+
  5. | title | text | indexed stored |
  6. +-------+------+----------------+
  7. 1 row in set (0.00 sec)