Tag: Indexing

Purging old rows with QueryScript: three use cases

MySQL

Problem: you need to purge old rows from a table. This may be your weekly/monthly cleanup task. The table is large, the amount of rows to be deleted is large, and doing so in one big DELETE is too heavy.

You can use oak-chunk-update or pt-archiver to accomplish the task. You can also use server side scripting with QueryScript, offering a very simple syntax with no external scripting, dependencies and command line options.

I wish to present three cases of row deletion, with three different solutions. In all cases we assume some TIMESTAMP column exists in table, by which we choose to purge the row. In all cases we assume we wish to purge rows older than 1 month.

We assume the naive query is this:

DELETE FROM my_schema.my_table WHERE row_timestamp < CURDATE() - INTERVAL 1 MONTH

Case 1: TIMESTAMP column is indexed

I almost always index a timestamp column, if only for being able to quickly purge data (but usually also to slice data by date). In this case where the column is indexed, it’s very easy to figure out which rows are older than 1 month.

We break the naive query into smaller parts, and execute these in sequence: Continue reading » “Purging old rows with QueryScript: three use cases”

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”

common_schema rev. 68: eval(), processlist_grantees, candidate_keys, easter_day()

MySQL

Revision 68 of common_schema is out, and includes some interesting features:

  • eval(): Evaluates the queries generated by a given query
  • match_grantee(): Match an existing account based on user+host
  • processlist_grantees: Assigning of GRANTEEs for connected processes
  • candidate_keys: Listing of prioritized candidate keys: keys which are UNIQUE, by order of best-use.
  • easter_day(): Returns DATE of easter day in given DATETIME’s year.

Let’s take a slightly closer look at these:

eval()

I’ve dedicated this blog post on MySQL eval() to describe it. In simple summary: eval() takes a query which generates queries (most common use queries on INFORMATION_SCHEMA) and auto-evaluates (executes) those queries. Read more

match_grantee()

As presented in Finding CURRENT_USER for any user, I’ve developed the algorithm to match a connected user+host details (as presented with PROCESSLIST) with the grantee tables (i.e. the mysql.user table), in a manner which simulates the MySQL server account matching algorithm.

This is now available as a stored function: given a user+host, the function returns with the best matched grantee. Read more

processlist_grantees

This view relies on the above, and maps the entire PROCESSLIST onto GRANTEEs. The view maps each process onto the GRANTEE (MySQL account) which is the owner of that process. Surprisingly, MySQL does not provide one with such information. Continue reading » “common_schema rev. 68: eval(), processlist_grantees, candidate_keys, easter_day()”

Announcing common_schema: common views & routines for MySQL

MySQL

Today I have released common_schema, a utility schema for MySQL which includes many views and functions, and is aimed to be installed on any MySQL server.

What does it do?

There are views answering for all sorts of useful information: stuff related to schema analysis, data dimensions, monitoring, processes & transactions, security, internals… There are basic functions answering for common needs.

Some of the views/routines simply formalize those queries we tend to write over and over again. Others take the place of external tools, answering complex questions via SQL and metadata. Still others help out with SQL generation.

Here are a few highlights:

There’s more. Take a look at the common_schema documentation for full listing. And it’s evolving: I’ve got quite a few ideas already for future components.

Some of these views rely on heavyweight INFORMATION_SCHEMA tables. You should be aware of the impact and risks.

What do I need to install?

There’s no script or executable file. It’s just a schema. The distribution in an SQL file which generates common_schema. Much like a dump file.

Continue reading » “Announcing common_schema: common views & routines for MySQL”

Reasons to use AUTO_INCREMENT columns on InnoDB

MySQL

An InnoDB table must have a primary key (one is created if you don’t do it yourself). You may have a natural key at hand. Stop! Allow me to suggest an AUTO_INCREMENT may be better.

Why should one add an AUTO_INCREMENT PRIMARY KEY on a table on which there’s a natural key? Isn’t an AUTO_INCREMENT a pseudo key, meaning, it doesn’t have any explicit relation to the row data, other than it is a number and unique?

Yes, indeed so. Nevertheless, consider:

  • Natural keys are many times multi-columned.
  • Multi column PRIMARY KEYs make for larger keys, and make for bloated secondary keys as well. You may be wasting space for storing the additional AUTO_INCREMENT column, but you may gain space back on secondary keys.
  • Multi column PRIMARY KEYs make for more locks. See also this post.
  • InnoDB INSERTs work considerably faster when worked in ascending PRIMARY KEY order. Can you ensure your natural key is in such order?
  • Even though an AUTO_INCREMENT makes for an INSERT bottleneck (values must be given serially), it is in particular helpful to InnoDB by ensuring PRIMARY KEY values are in ascending order.
  • AUTO_INCEMENT makes for chronological resolution. You know what came first, and what came next.
  • In many datasets, more recent entries are often being accessed more, and are therefore “hotter”. By using AUTO_INCREMENT, you’re ensuring that recent entries are grouped together within the B+ Tree. This means less random I/O when looking for recent data.
  • A numerical key is in particular helpful in splitting your table (and tasks on your table) into smaller chunks. I write tools which can work out with any PRIMARY KEY combination, but it’s easier to work with numbers.

Multi condition UPDATE query

MySQL

A simple question I’ve been asked:

Is it possible to merge two UPDATE queries, each on different WHERE conditions, into a single query?

For example, is it possible to merge the following two UPDATE statements into one?

mysql> UPDATE film SET rental_duration=rental_duration+1 WHERE rating = 'G';
Query OK, 178 rows affected (0.01 sec)

mysql> UPDATE film SET rental_rate=rental_rate-0.5 WHERE length < 90;
Query OK, 320 rows affected (0.01 sec)

To verify our tests, we take a checksum:

mysql> pager md5sum
PAGER set to 'md5sum'
mysql> SELECT film_id, title, rental_duration, rental_rate FROM film ORDER BY film_id;
c2d253c3919efaa6d11487b1fd5061f3  -

Obviously, the following query is incorrect: Continue reading » “Multi condition UPDATE query”

Simple guideline for choosing appropriate InnoDB PRIMARY KEYs

MySQL

Risking some flames, I’d like to suggest only two options for choosing PRIMARY KEYs for InnoDB tables. I suggest they should cover 99% (throwing numbers around) of cases.

PRIMARY KEY cases

  1. An integer (SMALLINT / INT / BIGINT), possibly AUTO_INCREMENT column.
  2. The combination of two columns on a many-to-many connecting table (e.g. film_actor, which connects films to actors), the two columns being the PRIMARY KEYs of respective data tables. This rule may be extended to 3-way relation tables.

A short recap: an InnoDB must have a PRIMARY KEY. It will pick one if you don’t offer it. It can pick a really bad UNIQUE KEY (e.g. website_url(255)) or make one up using InnoDB internal row ids. If you don’t have a good candidate, an AUTO_INCREMENT PRIMARY KEY is probably the easiest way out.

A 2-column combination for a many-to-many connection table is common and viable. The PRIMARY KEY will not only provide with good join access method, but will also provide with the required UNIQUE constraint.

An integer-based PRIMARY KEY will make for more compact & shallow index tree structures, which leads to less I/O and page reads.

An AUTO_INCREMENT will allow for ascending PRIMARY KEY order of INSERT, which is InnoDB-friendly: index pages will be more utilized, less fragmented. Continue reading » “Simple guideline for choosing appropriate InnoDB PRIMARY KEYs”

Thoughts and ideas for Online Schema Change

Development MySQL

Here’s a few thoughts on current status and further possibilities for Facebook’s Online Schema Change (OSC) tool. I’ve had these thoughts for months now, pondering over improving oak-online-alter-table but haven’t got around to implement them nor even write them down. Better late than never.

The tool has some limitations. Some cannot be lifted, some could. Quoting from the announcement and looking at the code, I add a few comments. I conclude with a general opinion on the tool’s abilities.

“The original table must have PK. Otherwise an error is returned.”

This restriction could be lifted: it’s enough that the table has a UNIQUE KEY. My original oak-online-alter-table handled that particular case. As far as I see from their code, the Facebook code would work just as well with any unique key.

However, this restriction is of no real interest. As we’re mostly interested in InnoDB tables, and since any InnoDB table should have a PRIMARY KEY, we shouldn’t care too much.

“No foreign keys should exist. Otherwise an error is returned.”

Tricky stuff. With oak-online-alter-table, changes to the original table were immediately reflected in the ghost table. With InnoDB tables, that meant same transaction. And although I never got to update the text and code, there shouldn’t be a reason for not using child-side foreign keys (the child-side is the table on which the FK constraint is defined).

The Facebook patch works differently: it captures changes and writes them to a delta table,  to be later (asynchronously) analyzed and make for a replay of actions on the ghost table. Continue reading » “Thoughts and ideas for Online Schema Change”

How often should you use OPTIMIZE TABLE? – followup

MySQL

This post follows up on Baron’s How often should you use OPTIMIZE TABLE?. I had the opportunity of doing some massive purging of data from large tables, and was interested to see the impact of the OPTIMIZE operation on table’s indexes. I worked on some production data I was authorized to provide as example.

The use case

I’ll present a single use case here. The table at hand is a compressed InnoDB table used for logs. I’ve rewritten some column names for privacy:

mysql> show create table logs \G

Create Table: CREATE TABLE `logs` (
 `id` int(11) NOT NULL AUTO_INCREMENT,
 `name` varchar(20) CHARACTER SET ascii COLLATE ascii_bin NOT NULL,
 `ts` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
 `origin` varchar(64) CHARACTER SET ascii COLLATE ascii_bin NOT NULL,
 `message` text NOT NULL,
 `level` tinyint(11) NOT NULL DEFAULT '0',
 `s` char(16) CHARACTER SET ascii COLLATE ascii_bin NOT NULL DEFAULT '',
 PRIMARY KEY (`id`),
 KEY `s` (`s`),
 KEY `name` (`name`,`ts`),
 KEY `origin` (`origin`,`ts`)
) ENGINE=InnoDB AUTO_INCREMENT=186878729 DEFAULT CHARSET=utf8 ROW_FORMAT=COMPRESSED KEY_BLOCK_SIZE=8

The table had log records starting 2010-08-23 and up till 2010-09-02 noon. Table status: Continue reading » “How often should you use OPTIMIZE TABLE? – followup”