Tag: INFORMATION_SCHEMA

Baffling 5.7 global/status variables issues, unclean migration path

MySQL

MySQL 5.7 introduces a change in the way we query for global variables and status variables: the INFORMATION_SCHEMA.(GLOBAL|SESSION)_(VARIABLES|STATUS) tables are now deprecated and empty. Instead, we are to use the respective performance_schema.(global|session)_(variables|status) tables.

But the change goes farther than that; there is also a security change. Oracle created a pitfall of 2 changes at the same time:

  1. Variables/status moved to a different table
  2. Privileges required on said table

As an example, my non-root user gets:

mysql> show session variables like 'tx_isolation';
ERROR 1142 (42000): SELECT command denied to user 'normal_user'@'my_host' for table 'session_variables'

Who gets affected by this? Nearly everyone and everything.

  • Your Nagios will not be able to read status variables
  • Your ORM will not be able to determine session variables
  • Your replication user will fail connecting (see this post by Giuseppe)
  • And most everyone else.

The problem with the above is that involves two unrelated changes to your setup, which are not entirely simple to coordinate:

  1. Change your app code to choose the correct schema (information_schema vs. performance_schema)
  2. GRANT the permissions on your database

Perhaps at this point you still do not consider this to be a problem. You may be thinking: well, let’s first prepare by creating the GRANTs, and once that is in place, we can, at our leisure, modify the code.

Not so fast. Can you really that simply create those GRANTs? Continue reading » “Baffling 5.7 global/status variables issues, unclean migration path”

common_schema & openark-kit in the media: #DBHangOps, OurSQL

MySQL

#DBHangOps

I had the pleasure of joining into @DBHangOps today, and speak about common_schema and openark-kit. What was meant to be a 15 minute session turned to be 50 — sorry, people, I don’t talk as much at home, but when it comes to my pet projects…

I also realized I was missing on a great event: DBHangOps is a hangout where you can chat and discuss MySQL & related technologies with friends and colleagues, with whom you typically only meet at conferences. I will certainly want to attend future events.

Thanks to John Cesario and Geoffrey Anderson who invited me to talk, and to the friends and familiar faces who attended; I was happy to talk about my work, and very interested in hearing about how it’s being put to use. We also had time to discuss ps_helper with no other than Mark Leith!

The video is available on Twitter/YouTube.

OurSQL

openark-kit has also been featured on the OurSQL podcast by Sheeri & Gerry, who did great coverage of some tools. I will disclose that more is to come; I’m happy this is in capable hands and look further to hear the next episode!

 

Hierarchical data in INFORMATION_SCHEMA and derivatives

MySQL

Just how often do you encounter hierarchical data? Consider a table with some parent-child relation, like the this classic employee table:

CREATE TABLE employee (
  employee_id INT UNSIGNED PRIMARY KEY,
  employee_name VARCHAR(100),
  manager_id INT UNSIGNED,
  CONSTRAINT `employee_manager_fk` FOREIGN KEY (manager_id) REFERENCES employee (employee_id)
) engine=innodb
;
+-------------+---------------+------------+
| employee_id | employee_name | manager_id |
+-------------+---------------+------------+
|           1 | Rachel        |       NULL |
|           2 | John          |          1 |
|           3 | Stan          |          1 |
|           4 | Naomi         |          2 |
+-------------+---------------+------------+

Questions like “What is the (nested) list of employees managed by Rachel?” or “Get Naomi’s managers chain of command” are classical questions. There are sometimes dependencies: if John leaves, does that mean Naomi loses her job, or does she get promoted, or something else? If John and Stan are on sick leave, does Rachel have any reason to come to work?

Hierarchical data is not limited to a single-table structure. Sometimes it takes a combination of a few tables (it’s just a JOIN) to make out the parent-child relation.

Hierarchical data is difficult to manage with SQL. This is especially true for MySQL, which does not support the WITH recursive query syntax.

Where can you find hierarchical data?

Even if you do not provide it by yourself, MySQL’s INFORMATION_SCHEMA has some for you. Partly obvious, partly implicit, here are some examples: Continue reading » “Hierarchical data in INFORMATION_SCHEMA and derivatives”

Killing InnoDB idle transactions

MySQL

The issue of terminating long-time idle open InnoDB transaction has been discussed recently by many. I wish to add my share, by proposing a quick and clean solution via common_schema.

common_schema 1.2 provides with the innodb_transactions view, which relies on INNODB_TRX – one of the InnoDB Plugin views in INFORMATION_SCHEMA – as well as on PROCESSLIST, and so is able to determine with certainty that a transaction has been idle for a long time.

innodb_transactions offers us with a sql_kill_query column, which produces a ‘KILL QUERY 12345’ type of value. So we can:

SELECT sql_kill_query FROM innodb_transactions WHERE trx_idle_seconds >= 10; 
+-------------------+
| sql_kill_query    |
+-------------------+
| KILL QUERY 292509 |
| KILL QUERY 292475 |
+-------------------+

common_schema‘s useful eval() routine allows us to actually invoke those KILL statements, all in a one-liner:

call eval('SELECT sql_kill_query FROM innodb_transactions WHERE trx_idle_seconds >= 10');

Technical details Continue reading » “Killing InnoDB idle transactions”

How common_schema split()s tables – internals

MySQL

This post exposes some of the internals, and the SQL behind QueryScript’s split. common_schema/QueryScript 1.1 introduces the split statement, which auto-breaks a “large” query (one which operates on large tables as a whole or without keys) into smaller queries, and executes them in sequence.

This makes for easier transactions, less locks held, potentially (depending on the user) more idle time released back to the database. split has similar concepts to oak-chunk-update and pt-archiver, but works differently, and implemented entirely in SQL on server side.

Take the following statement as example:

split (UPDATE sakila.inventory SET last_update = last_update + INTERVAL 6 HOUR)
  pass;

It yields with (roughly) the following statements:

UPDATE sakila.inventory SET last_update = last_update + INTERVAL 6 HOUR WHERE ((((`inventory`.`inventory_id` > '1')) OR ((`inventory`.`inventory_id` = '1'))) AND (((`inventory`.`inventory_id` < '1000')) OR ((`inventory`.`inventory_id` = '1000'))));
UPDATE sakila.inventory SET last_update = last_update + INTERVAL 6 HOUR WHERE ((((`inventory`.`inventory_id` > '1000'))) AND (((`inventory`.`inventory_id` < '2000')) OR ((`inventory`.`inventory_id` = '2000'))));
UPDATE sakila.inventory SET last_update = last_update + INTERVAL 6 HOUR WHERE ((((`inventory`.`inventory_id` > '2000'))) AND (((`inventory`.`inventory_id` < '3000')) OR ((`inventory`.`inventory_id` = '3000'))));
UPDATE sakila.inventory SET last_update = last_update + INTERVAL 6 HOUR WHERE ((((`inventory`.`inventory_id` > '3000'))) AND (((`inventory`.`inventory_id` < '4000')) OR ((`inventory`.`inventory_id` = '4000'))));
UPDATE sakila.inventory SET last_update = last_update + INTERVAL 6 HOUR WHERE ((((`inventory`.`inventory_id` > '4000'))) AND (((`inventory`.`inventory_id` < '4581')) OR ((`inventory`.`inventory_id` = '4581'))));

(I say “roughly” because internally there are user defined variables at play, but for convenience, I verbose the actual values as constants.)

How does that work?

common_schema works on server side. There is no Perl script or anything. It must therefore use server-side operations to:

  • Identify table to be split
  • Analyze the table in the first place, deciding how to split it
  • Analyze the query, deciding on how to rewrite it
  • Split the table (logically) into unique and distinct chunks
  • Work out the query on each such chunk

Following is an internal look at how common_schema does all the above. Continue reading » “How common_schema split()s tables – internals”

Table split(…) for the masses

MySQL

(pun intended)

common_schema‘s new split statement (see release announcement) auto-splits complex queries over large tables into smaller ones: instead of issuing one huge query, split breaks one’s query into smaller queries, each working on a different set of rows (a chunk).

Thus, it is possible to avoid holding locks for long times, allowing for smaller transactions. It also makes for breathing space for the RDBMS, at times boosting operation speed, and at times prolonging operation speed at will.

In this post I show how split exposes itself to the user, should the user wish so.

split can manage queries of the following forms:

  • DELETE FROM table_name [WHERE]…
  • DELETE FROM table_name USING <multi table syntax> [WHERE]…
  • UPDATE table_name SET … [WHERE]…
  • UPDATE <multiple tables> SET … [WHERE]…
  • INSERT INTO some_table SELECT … FROM <single or multiple tables> [WHERE]…
  • REPLACE INTO some_table SELECT … FROM <single or multiple tables> [WHERE]…
  • SELECT … FROM <multiple tables> [WHERE]…

The latter being a non-obvious one at first sight.

Basically, it’ automatic

You just say:

split (UPDATE sakila.inventory SET last_update = last_update + INTERVAL 6 HOUR)
  throttle 2;

And split identifies sakila.inventory as the table which needs to be split, and injects appropriate conditions so as to work on a subset of the rows, in multiple steps.

By the way, here’s how to execute a QueryScript code like the above. Continue reading » “Table split(…) for the masses”

Auto caching INFORMATION_SCHEMA tables: seeking input

MySQL

The short version

I have it all working. It’s kind of magic. But there are issues, and I’m not sure it should even exist, and am looking for input.

The long version

In Auto caching tables I presented with a hack which allows getting cached or fresh results via a simple SELECT queries.

The drive for the above hack was INFORMATION_SCHEMA tables. There are two major problems with INFORMATION_SCHEMA:

  1. Queries on schema-oriented tables such as TABLES, COLUMNS, STATISTICS, etc. are heavyweight. How heavyweight? Enough to make a lockdown of your database. Enough to crash down your database in some cases.
  2. The data is always generated on-the-fly, as you request it. Query the COLUMNS table twice, and risk two lockdowns of your database.

The auto-cache mechanism solves issue #2. I have it working, time based. I have an auto-cache table for each of the INFORMATION_SCHEMA heavyweight tables. Say, every 30 minutes the cache is invalidated. Throughout those 30 minutes, you get a free pass!

The auto-cache mechanism also paves the road to solving issue #1: since it works by invoking a stored routine, I have better control of the way I read INFORMATION_SCHEMA. This, I can take advantage of INFORMATION_SCHEMA optimization. It’s tedious, but not complicated.

For example, if I wanted to cache the TABLES table, I don’t necessarily read the entire TABLES data in one read. Instead, I can iterate the schemata, get a list of table names per schema, then read full row data for these, table by table. The result? Many many more SELECTs, but more optimized, and no one-big-lock-it-all query.

And the problem is…

Continue reading » “Auto caching INFORMATION_SCHEMA tables: seeking input”

INFORMATION_SCHEMA Optimizations: still crashing my servers

MySQL

[Update: need to take more breaks: now NOT crashing my servers! See clarifications below]

INFORMATION_SCHEMA Optimizations are meant to make your INFORMATION_SCHEMA queries lighter and safer.

For example, if you’re going to query the COLUMNS table for just the columns of a single table, then the following:

SELECT * FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA='sakila' AND TABLE_NAME='rental'

makes for an optimization: specifying a literal on TABLE_SCHEMA avoid scanning the directories of other schemata. Specifying a literal on TABLE_NAME avoids checking up on other tables. So it’s a one-schema-one-table read operation, as opposed to “first read every single column from all and any single schema and table, then return only those I’m interested in”.

Here’s the execution plan for the above query:

*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: COLUMNS
         type: ALL
possible_keys: NULL
          key: TABLE_SCHEMA,TABLE_NAME
      key_len: NULL
          ref: NULL
         rows: NULL
        Extra: Using where; Open_frm_only; Scanned 0 databases

What I tried to do is to read the entire COLUMNS table, one schema at a time, one table at a time. I’m good with this taking longer time.

I have a production system on which reads from COLUMNS consistently crash the servers. Well, one read at a time can’t do harm, right? Continue reading » “INFORMATION_SCHEMA Optimizations: still crashing my servers”

More MySQL foreach()

Development MySQL

In my previous post I’ve shown several generic use cases for foreach(), a new scripting functionality introduced in common_schema.

In this part I present DBA’s handy syntax for schema and table operations and maintenance.

Confession: while I love INFORMATION_SCHEMA‘s power, I just hate writing queries against it. It’s just so much typing! Just getting the list of tables in a schema makes for this heavy duty query:

SELECT TABLE_NAME FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_SCHEMA='sakila' AND TABLE_TYPE='BASE TABLE';

When a join is involved this really becomes a nightmare. I think it’s cumbersome, and as result, many do not remember the names and meaning of columns, making for “oh, I need to read the manual all over again just to get that query right”. Anyway, that’s my opinion.

A SHOW TABLES statement is easier to type, but cannot be integrated into a SELECT query (though we have a partial solution for that, too), and besides, when filtering out the views, the SHOW statement becomes almost as cumbersome as the one on INFORMATION_SCHEMA.

Which is why foreach() offers handy shortcuts to common iterations on schemata and tables, as follows:

Use case: iterate all databases

call foreach('schema', 'CREATE TABLE ${schema}.event(event_id INT, msg VARCHAR(128))');

In the above we execute a query on each database. Hmmm, maybe not such a good idea to perform this operation on all databases? Let’s filter them:

Use case: iterate databases by name match

call foreach('schema like wordpress_%', 'ALTER TABLE ${schema}.wp_posts MODIFY COLUMN comment_author VARCHAR(96) NOT NULL');

The above will only iterate my WordPress databases (I have several of these), performing an ALTER on wp_posts for each of those databases. Continue reading » “More MySQL foreach()”

Reading results of SHOW statements, on server side

MySQL

SHOW statements are show stoppers on server side. While clients can get a SHOW statement as a result set just as any normal SELECT, things are not as such on server side.

On server side, that is, from within MySQL itself, one cannot:

SELECT `Database` FROM (SHOW DATABASES);

One cannot:

DECLARE show_cursor CURSOR FOR SHOW TABLES;

One cannot:

SHOW TABLES INTO OUTFILE '/tmp/my_file.txt';

So it is impossible to get the results with a query; impossible to get the results from a stored routine; impossible to get the results by file reading…

Bwahaha! A hack!

For some SHOW statements, there is a way around this. I’ve been banging my head against the wall for weeks now on this. Now I have a partial solution: I’m able to read SHOW output for several SHOW statements. Namely, those SHOW statements which allow a LIKE or a WHERE clause.

For example, most are familiar with the following syntax:

USE mysql;
SHOW TABLE STATUS LIKE 'user';

However not so many know that any SHOW statement which accepts LIKE, can also accept WHERE: Continue reading » “Reading results of SHOW statements, on server side”