November 20, 2009

Paul McCullagh answers your questions about PBXT

Posted by Morgan Tocker |

Following on from our earlier announcement, Paul McCullagh has responded with the answers to your questions – as well as a few I gathered from other Percona folks, and attendees of OpenSQL Camp. Thank you Paul!

What’s the “ideal” use case for the PBXT engine, and how does it compare in performance?  When would I use PBXT instead of a storage engine like MyISAM, InnoDB or XtraDB?

Unfortunately it is not possible to point to a specific category of applications and say, “PBXT will be better here, so try it”.  PBXT is a general purpose transactional storage engine, designed to perform well on a broad range of tasks, much like InnoDB.  However, PBXT’s log-based architecture makes performance characteristics different to both MyISAM and InnoDB/XtraDB. Tests show that PBXT’s performance is similar to InnoDB but, depending on your database designed and the application, it can be faster.

PBXT is a community project and, of course, we depend on users trying it out. In the long run, this will determine to what extent we are able to continue to develop and improve the engine.  So, despite this rather vague answer, we are hoping that more people try it out, and work with us to improve the engine as necessary.  My thanks to all who are already doing this! :)

I think I remember reports that PBXT (at an early stage) out performed InnoDB with INSERTS and UPDATES (but not SELECTS). That would make PBXT very interesting for non-SELECT-intensive applications (finance, production management etc.) in my opinion.  Is this the case, and do you have any recent benchmarks available?

This is no longer necessarily the case. For example a test (http://mysqlha.blogspot.com/2009/03/pbxt-is-fast-no-kidding.html) by Mark Callaghan shows that PBXT can actually out perform InnoDB with SELECTs under circumstances.

The implementation of full-durability has changed the performance characteristics of PBXT from “MyISAM-like” to more InnoDB-like. Originally PBXT was conceived as an engine that would be somewhere between MyISAM and InnoDB in both performance and features. The early version of PBXT was not fully durable (equivalent to innodb_flush_log_at_trx_commit=2).

A major change was completed at the beginning of last year with the implementation of full-durability. In doing this it was important to keep the log-based architecture which was the reason for the high write performance of earlier versions.

Traditional transactional implementations suffer from the problem that a backlog of asynchronous writes accumulate until it swamps the engine. There has been a lot of work on both InnoDB and XtraDB to solve this problem. The key words here are fuzzy and adaptive checkpointing (the former, originally implementation by Heiki for InnoDB, and the latter, an excellent addition to XtraDB).

Both methods improve the management of the asynchronous writes. The idea behind the log-based solution, on the other hand, is to avoid the accumulating a backlog of asynchronous writes, but writing synchronously.

Although write performance is comparable with InnoDB, I am not entirely convinced that PBXT’s implementation of the log-based I/O is optimal at this stage. This is ongoing work for PBXT 1.5.

Morgan notes: As well as Adaptive Checkpointing, Oracle has also been working on Adaptive Flushing for the InnoDB Plugin. The engine being ’swamped’ problem that Paul is referring to is best described visually – see this post for more info.

What were the hard decisions or trade-offs that you had to make when designing PBXT?

If you read the white paper from 2006 (http://primebase.org/download/pbxt_white_paper.pdf) you will notice that the original design was uncompromisingly MVCC-based.  Some of this has been changed to make PBXT more InnoDB-like, but other principles have remained.

Pure-MVCC does not do any locking. Read locks are not required because each transaction effectively gets its own snapshot of the database. And write locks are not acquired when updating. Instead, the application can hit a “optimistic lock error” if a record is updated by another user.

Now PBXT does acquire locks for 2 reasons: to support SELECT FOR UPDATE, and to avoid optimistic locking errors. This makes PBXT’s behavior identical to InnoDB in REPEATABLE READ mode.

On the other hand, there are currently no plans to implement InnoDB style “gap locking”. Gap locking effectively involves locking rows that do not exist. This, in turn, means that PBXT transactions are not SERIALIZABLE.  A result of this is that statement-based replication is not supported by the engine.

Another hard decision was not to implement clustered indexes which I mentioned in more details later.

How does online backup work in PBXT, and is incremental backup possible?

A recent version of PBXT (1.0.09) supports the MySQL Backup API which was originally implemented in MySQL 6.0. This feature is now scheduled for an upcoming version of MySQL 5.4.

The Backup API makes it possible to pull a consistent snapshot of an entire database even when tables use different engine types. The API does not yet support incremental backup, but this is planned.

Internally this feature is implemented by PBXT using an MVCC-based consistent snapshot.

Does PBXT have a maintenance thread like InnoDB’s main thread?

PBXT has several system threads, that are responsible for various maintenance tasks. The most important of these are the “Writer”, the “Sweeper” and the “Checkpointer”;

  • The Writer transfers data from the transaction log to the database table files. This task runs constantly and is the main source of asynchronous I/O in the engine.
  • The Sweeper is a thread unique to the PBXT architecture. It’s job is to clean up after a transaction. Basically it recognizes which record versions are no longer needed and deletes them from the database.
  • The Checkpointer flushes the data written by the Writer to disk. It is also responsible for writing consistent snapshots of the index files to disk. The frequency of checkpoints, as with other engines like InnoDB, determines the recovery time.

Does PBXT support clustered indexes?

No, currently it does not. This is one of the original design decisions (as raised by a previous question).  Two things contributed to this decision:

  • Supporting clustered indexes would have made the implementation of PBXT more complicated than I would have liked. My goal was to make PBXT as simple as possible, so that it is easy to maintain the code and add features.
  • I believe clustered indexes are becoming less relevant with the rise of Solid State technology. As random read access times decrease clustering of data will become less and less important.

What is the page size in PBXT, and can it be tuned?

PBXT uses 16K pages for the index data and (approximately) 32K pages for the table data. Both sizes can be set using compile time switches. However, if the index page size is changed, then the indices need to be rebuilt, which can be done by REPAIR TABLE.  The table data page size does not require a rebuild because a page of records in the table is just a group of records (not an actual fixed length page).

Are there any differences in the PBXT implementation of MVCC that might surprise experienced InnoDB DBAs?  Also – In MVCC does it keep the versions in indexes, and can PBXT use MVCC for index scans?

If you are using InnoDB in REPEATABLE READ mode, then there is essentially no difference in the isolation paradigm between the two engines.

REPEATABLE READ is often preferred over SERIALIZABLE mode because it allows a greater degree of concurrency while still providing the necessary transaction isolation. So I do not consider the lack of serializability as a serious deficit in the engine. And, fortunately MySQL 5.1. supports row-based replication which makes it possible to do replication while using REPEATABLE READ.

PBXT does use MVCC to do index scans. Basically this means that all types of SELECTs can be done without locking.

Morgan notes: Indexes not using MVCC is one of the main differences in the Falcon storage engine.

PBXT supports row-level locking and foreign keys. Does this create any additional locking overhead that we should be aware of?

Firstly, PBXT does not acquire read locks. A normal SELECT does not lock at all. In addition, an UPDATE or DELETE only acquires a temporary row-lock. This lock is released when the row is updated or deleted because the MVCC system can detect that a row has been changed (and is therefore write locked).

This means that PBXT does not normally need to maintain long lists of row-level locks. This is also the case when a foreign key leads to cascading operations which can affect thousands of rows.

The only case you need to be aware of is SELECT FOR UPDATE. This operation acquires and holds a row-level lock for each row returned by the SELECT. These locks are all stored in RAM. The format is quite compact (especially when row IDs are consecutive) but this can become an issue if millions of rows are selected in this manner.

I should also mention that the consequence of this is that SELECT … LOCK IN SHARE MODE is currently not supported.

When I evaluate a storage engine my key acceptance criteria are things like backup, concurrency, ACID compliance and crash recovery. As a storage engine developer, what other criteria do you think I should be adding?

Yes, I think you have mentioned the most important criteria. What I can add to this list are 3 things that make developing a storage engine extremely demanding: performance, stability and data integrity.

Of course, as a DBA or database user these aspects are so basic that they are taken for granted.

But engine developers need to keep performance, stability and data integrity in mind constantly. The problem is, they compete with each other: increasing performance often causes instabilities that then have to be fixed. How to optimize the program without compromising data integrity is a constant question.

Relative to maintaining performance, stability and data integrity, adding features to an engine is easy. So I would say that these are the criteria that concern a developer the most.

MySQL supports a “pluggable storage engine API”, but it seems that not all the storage engine vendors are able to keep all their code at that layer (Infobright had to make major changes to MySQL itself).  What war stories can you report on in plugging into MySQL?

Unfortunately the “war” continues. I have already received several e-mails that PBXT does not compile with the recently released MySQL 5.1.41!

Any dot release can lead to this problem, and I think PBXT is fairly moderate with its integration into MySQL.

My main advantage: I have been able to avoid modifying any part of MySQL to make the engine work. This means that PBXT runs with the standard MySQL/MariaDB distribution.

But this has required quite a bit of creative work, in other words, hacks.

One of the main problems has been running into global locks when calling back into MySQL to do things like open a table, create a session structure (THD) or create a .frm file.

One extreme example of this is PBXT recovery. When MySQL calls the engine “init” method on startup it is holding the global LOCK_plugin lock. In init, the engine needs to do recovery. In PBXT’s case this means opening tables (reading a .frm file), which requires creating a THD. The code to create a THD in turn tries to acquire LOCK_plugin!

Unfortunately a thread hangs if it tries to acquire the same mutex twice, so this just does not work!

We went through quite a few iterations (MySQL code was also changing during the development of 5.1) before we came up with the current solution: create a background thread to do recovery asynchronously. So the thread can wait for the LOCK_plugin to be unlocked before it continues.

The affect is that the init function returns quickly, but the first queries that access PBXT tables may hang waiting for recovery to complete.

PBXT seems to have very few configuration parameters.  Was this an intentional design decision, and do you see it creating opportunities for you in organizations with less internal IT-expertise?

No, this is not by design.

While I try to only add tuning parameters that are absolutely necessary, PBXT is not specifically designed to be self-tuning, because I believe that is a very hard problem to solve in general.

Tuning parameters are often added to an engine in response to performance problems in particular configurations. This is not necessarily a bad thing because it provides DBA’s with the tools they need.

My goal for PBXT in this regard is twofold:

  1. I hope that most installations can get away with setting a minimum of tuning parameters, in particular, just the cache values.
  2. On the other hand, I aim to provide expert tuning parameters for installations that need to extract maximum performance from the hardware. This work is ongoing.

Morgan notes: There are more in InnoDB/XtraDB now than there were three years ago. This is probably something that emerges over time as we get to understand more about an engine.

November 16, 2009

Interviews for InfiniDB and TokuDB are next

Posted by Morgan Tocker |

I forwarded on a list of questions about PBXT to Paul McCullagh today.  While Paul’s busy answering them, I’d like to announce that Robert Dempsey (InfiniDB storage engine) and Bradley C. Kuszmaul (TokuDB storage engine) have also accepted an interview. If you have any questions about either storage engine, please post them here by Friday 20th November.

July 20, 2009

XtraDB storage engine release 1.0.3-6

Posted by Aleksandr Kuzminsky |

Dear community,

Today we are pleased to announce release 6 of XtraDB – the result of 2 months hard work.

The release includes following new features:

  • MySQL 5.1.36 as a base release
  • New patch innodb_recovery_patches.patch
  • Experimental adaptive checkpoint method estimate
  • innodb_stats – the implementation of the fix forMySQL Bug#30423
  • expand-import Support of import InnoDB / XtraDB tables from another server
  • split-bufpool-mutex-3 New patch to split buffer pool mutex
  • g-style-io-thread Google’s fixes to InnoDB IO threads
  • dict-size-limit Limit of internal data dictionary

Fixed bugs:

The builds for RedHat4,5 and Debian are located on http://www.percona.com/mysql/xtradb/5.1.36-6/
The latest source code of XtraDB, including development branch you can find on LaunchPAD.

Please report any bugs found on Bugs in Percona XtraDB Storage Engine for MySQL.
For general questions use our Pecona-discussions group, and for development question Percona-dev group.

For support, commercial and sponsorship inquiries contact Percona

June 11, 2009

The feature I love in TokuDB

Posted by Vadim |

Playing with TokuDB updates I noticed in SHOW PROCESSLIST unsual for MySQL State.

CODE:
  1. mysql> show processlist;
  2. +----+------+-----------+--------+---------+------+---------------------------+-----------------------------+
  3. | Id | User | Host      | db     | Command | Time | State                     | Info                        |
  4. +----+------+-----------+--------+---------+------+---------------------------+-----------------------------+
  5. 3 | root | localhost | sbtest | Query   |   30 | Updated about 764000 rows | update sbtest set email=zip |
  6. ...
  7. mysql> show processlist;
  8. +----+------+-----------+--------+---------+------+----------------------------+-----------------------------+
  9. | Id | User | Host      | db     | Command | Time | State                      | Info                        |
  10. +----+------+-----------+--------+---------+------+----------------------------+-----------------------------+
  11. 3 | root | localhost | sbtest | Query   |   79 | Updated about 1900000 rows | update sbtest set email=zip |
  12. ...

(Do not look in stupid UPDATE query, it's just for testing :) )

So looking in SHOW PROCESSLIST you can see progress of query execution.

I would want to see it in standard MySQL and InnoDB more than all these triggers and stored routines! Probably will implement this in XtraDB.

April 28, 2009

Detailed review of Tokutek storage engine

Posted by Vadim |

(Note: Review was done as part of our consulting practice, but is totally independent and fully reflects our opinion)

I had a chance to take look TokuDB (the name of the Tokutek storage engine), and run some benchmarks. Tuning of TokuDB is much easier than InnoDB, there only few parameters to change, and actually out-of-box things running pretty well.

There are some rumors circulating that TokuDB is ”.. only an in memory or read-only engine, and that's why inserts are so fast”. This is not actually the case, as TokuDB is a disk-based, read-write transactional storage engine that is based on special “fractal tree indexes”. Fractal Trees are a drop-in-replacement for a B-tree (based on current research in data structures by professors at Stony Brook, Rutgers, and MIT). I can't say exactly how it is improved, because the engine itself is closed source.
[read more...]

April 15, 2009

How to decrease InnoDB shutdown times

Posted by Baron Schwartz |

Sometimes a MySQL server running InnoDB takes a long time to shut down. The usual culprit is flushing dirty pages from the buffer pool. These are pages that have been modified in memory, but not on disk.

If you kill the server before it finishes this process, it will just go through the recovery phase on startup, which can be even slower in stock InnoDB than the shutdown process, for a variety of reasons.

[read more...]

April 8, 2009

XtraDB storage engine release 1.0.3-4 codename Sakura

Posted by Evgeniy Stepchenko |

Today we glad to announce release 1.0.3-4 of our XtraDB storage engine.

Here is a list of enhancements in this release:

Percona XtraDB 1.0.3-4 (Sakura) available in source and several binary packages.

XtraDB is compatible with existing InnoDB tables (unless you used innodb_extra_undoslots) and we are going to keep compatibility in further releases. We are open for features requests for new engine and ready to accept community patches. You can monitor Percona’s current tasks and further plans on the Percona XtraDB Launchpad project. You can also request features and report bugs there. Also we have setup two maillists for General discussions and for Development related questions.

April 6, 2009

MySQL and IBM

Posted by Vadim |

No, this is not about Sun and IBM :) This is about MySQL. If you download latest 5.1.33 source code you may find there storage/ibmdb2i directory, which obviously is IBM DB2 related. Interesting that there is no mentioning of new engine in Announcement http://dev.mysql.com/doc/refman/5.1/en/news-5-1-33.html.
Quick look into source code says

CODE:
  1. MYSQL_STORAGE_ENGINE([ibmdb2i], [], [IBM DB2 for i Storage Engine],                                                               
  2.         [IBM DB2 for i Storage Engine], [max,max-no-ndb])                                                                         
  3. MYSQL_PLUGIN_DYNAMIC([ibmdb2i], [ha_ibmdb2i.la])

Also interesting that license of added files is not GPL, but

CODE:
  1. /*
  2. Licensed Materials - Property of IBM
  3. DB2 Storage Engine Enablement
  4. Copyright IBM Corporation 2007,2008
  5. All rights reserved
  6. Redistribution and use in source and binary forms, with or without modification,
  7. are permitted provided that the following conditions are met:
  8. (a) Redistributions of source code must retain this list of conditions, the
  9.      copyright notice in section {d} below, and the disclaimer following this
  10.      list of conditions.
  11. (b) Redistributions in binary form must reproduce this list of conditions, the
  12.      copyright notice in section (d) below, and the disclaimer following this
  13.      list of conditions, in the documentation and/or other materials provided
  14.      with the distribution.
  15. (c) The name of IBM may not be used to endorse or promote products derived from
  16.      this software without specific prior written permission.
  17. (d) The text of the required copyright notice is:
  18.        Licensed Materials - Property of IBM
  19.        DB2 Storage Engine Enablement
  20.        Copyright IBM Corporation 2007,2008
  21.        All rights reserved
  22. THIS SOFTWARE IS PROVIDED BY IBM CORPORATION "AS IS" AND ANY EXPRESS OR IMPLIED
  23. WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
  24. MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT
  25. SHALL IBM CORPORATION BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
  26. EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT
  27. OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
  28. INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
  29. CONTRACT, STRICT LIABILITY, OR TORT INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
  30. IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY
  31. OF SUCH DAMAGE.
  32. */

I think this is outcome of 2-year old press release
"MySQL AB and IBM Announce Open Source Database Support for the IBM System i Platform", it just took a bit a while to put it into source tree. I wonder what happened with policy not accept significant changes into production release.

April 28, 2008

The MySQL optimizer, the OS cache, and sequential versus random I/O

Posted by Baron Schwartz |

In my post on estimating query completion time, I wrote about how I measured the performance on a join between a few tables in a typical star schema data warehousing scenario.

In short, a query that could take several days to run with one join order takes an hour with another, and the optimizer chose the poorer of the two join orders. Why is one join order so much slower than the other, and why did the optimizer not choose the faster one? That's what this post is about.

Let's start with the MySQL query optimizer. The optimizer tries to choose the best join order based on its cost metric; it tries to estimate the cost for a query, then choose the query plan that has the lowest cost. The unit of cost for the MySQL query optimizer is a single random 4k data page read. In general, it's a pretty good metric, but it has one major weakness: the server doesn't know whether a read will be satisfied from the operating system cache, or whether it'll have to go to disk. (This distinction is abstracted away by the storage engine; the optimizer doesn't know how a given storage engine stores its data).

I'll try to omit the details and keep this clean. Let's take a look at the tables.

SQL:
  1. mysql> SHOW TABLE STATUS LIKE 'fact'\G
  2. *************************** 1. row ***************************
  3. Name: fact
  4. Engine: MyISAM
  5. Rows: 147045493
  6. Avg_row_length: 117
  7. Data_length: 17217646764
  8. Index_length: 11993816064
  9.  
  10. mysql> SHOW TABLE STATUS LIKE 'dim1'\G
  11. *************************** 1. row ***************************
  12. Name: dim1
  13. Engine: MyISAM
  14. Rows: 453193
  15. Avg_row_length: 122
  16. Data_length: 55605116
  17. Index_length: 93812736
  18.  
  19. mysql> SHOW TABLE STATUS LIKE 'dim2'\G
  20. *************************** 1. row ***************************
  21. Name: dim2
  22. Engine: MyISAM
  23. Rows: 811
  24. Avg_row_length: 105
  25. Data_length: 85368
  26. Index_length: 154624

It's a big fact table and two fairly small dimension tables, which is normal. Here is the query:

SQL:
  1. SELECT fact.col1, min(fact.col2) AS min_col2
  2. FROM fact, dim1, dim2
  3. WHERE fact.col4 = dim1.col4
  4. AND dim1.col3 <> 'hello world'
  5. AND dim2.col5 = 1
  6. AND fact.dim2_id = dim2.dim2_id
  7. AND fact.col2> some_const
  8. GROUP BY fact.col1

There are indexes on all the columns in all the ways you'd expect: all the dimension columns are indexed on every table, and there's a separate index on every column in the WHERE clause. Here's the query plan initially.

SQL:
  1. *************************** 1. row ***************************
  2. TABLE: dim1
  3. type: range
  4. key_len: 195
  5. rows: 18790
  6. Extra: USING WHERE; USING TEMPORARY; USING filesort
  7. *************************** 2. row ***************************
  8. TABLE: fact
  9. type: ref
  10. key_len: 4
  11. rows: 606
  12. Extra: USING WHERE
  13. *************************** 3. row ***************************
  14. TABLE: dim2
  15. type: eq_ref
  16. key_len: 2
  17. rows: 1
  18. Extra: USING WHERE

This query will run for days and never complete. No one ever let it finish to see how long it would run.

How do I know it will run for days? Here's my train of thought:

  • It's performing index lookups into the fact table, which is big.
  • An index lookup is a random I/O.
  • A modern disk can do about 100 random I/O's per second, as a rule of thumb.
  • If you do the math with the rows column in EXPLAIN, you realize that this equates to about 18790 * 606 = 11386740 I/O operations, assuming that the indexes are fully in memory.
  • When you divide this by 100 I/O operations per second, and then divide that by 86400 seconds in a day, you get about 2.6 days.

Ouch! That's slow.

Now let's look at the alternative: table-scan the fact table, and do index lookups in the two dimension tables. MySQL doesn't want to choose this join order, so we'll force it with STRAIGHT_JOIN:

SQL:
  1. EXPLAIN SELECT STRAIGHT_JOIN  ....
  2. +-------+-----------+-----------+---------------------------------+
  3. | TABLE | type      | rows      | Extra                           |
  4. +-------+-----------+-----------+---------------------------------+
  5. | fact  | ALL       | 147367284 | USING TEMPORARY; USING filesort |
  6. | dim1  | eq_ref    | 1         | USING WHERE                     |
  7. | dim2  | eq_ref    | 1         | USING WHERE                     |
  8. +-------+-----------+-----------+---------------------------------+

As we saw in the previous post, which I linked at the top of this post, we can scan the fact table in less than an hour. And it turns out that joining to the dimension tables doesn't slow the query perceptibly, because these tables are small and they stay in memory, in the OS cache. (They don't get evicted from memory by the cache's LRU policy, because they are frequently used -- once per row in the fact table. The LRU policy evicts old blocks from the fact table instead, which is perfect -- these blocks are used only once and not needed again, so they can be replaced).

The difference between the two queries -- 55 minutes and 2.6 days -- is basically the difference between scanning data sequentially on disk and random disk I/O.

So now you know why one join order is faster than the other. But why didn't the optimizer know this, too? The optimizer does know that random access is slower than sequential access, but it doesn't know that the dimension tables will stay in memory, and this is an important distinction.

Let's put ourselves into the mindset of the optimizer. We'll assume that every join to the dimension tables will go to disk instead of being read from cache. Now the STRAIGHT_JOIN becomes a table scan of about 313 sequential reads (150 million rows / 117 bytes per row / 4096 bytes per read), plus about 150 million random I/Os for the first dimension table, plus 150 million random I/Os for the second dimension table. That's 300 million random I/O operations.

In contrast, the optimizer chose a plan that it thought would cause only 11.3 million random I/O operations.

The optimizer was being smart, given its lack of knowledge about the OS cache. This is why an expert is sometimes needed to provide the missing information. If the MySQL optimizer were right and each of these had to go to disk, our STRAIGHT_JOIN plan would take more than a month to complete! Good thing we know the difference between cache and disk!

December 19, 2007

MVCC: Transaction IDs, Log Sequence numbers and Snapshots

Posted by peter |

MySQL Storage Engines implementing Multi Version Concurrency Control have several internal identifiers related to MVCC. I see a lot of people being confused what they are and why they are needed so I decided to take a time to explain it a bit. This is general explanation, it does not corresponds to Innodb in particular and some implementation can be different but I hope this will let you to understand MVCC a bit better.
[read more...]