Advanced Query Tuning in MySQL 5.6 and MySQL 5.7 Webinar: Q&A

Thank you for attending my July 22 webinar titled “Advanced Query Tuning in MySQL 5.6 and 5.7” (my slides and a replay available here). As promised here is the list of questions and my answers (thank you for your great questions).

Q: Here is the explain example:

mysql> explain extended select id, site_id from test_index_id where site_id=1
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: test_index_id
         type: ref
possible_keys: key_site_id
          key: key_site_id
      key_len: 5
          ref: const
         rows: 1
     filtered: 100.00
        Extra: Using where; Using index

why is site_id a covered index for the query, given the fact that a) we are selecting “id”, b) key_site_id only contains site_id?

As the table is InnoDB, all secondary keys will always contain primary key (“id”); in this case the secondary index will contain all needed information to satisfy the above query and key_site_id will be “covered index”

Q: Applications change over time. Do you suggest doing a periodic analysis of indexes that are being used and drop the ones that are not? If yes, any suggestions as to tackle that?

Yes, that is a good idea. Usually it can be done easily with Percona toolkit or Performance_schema in MySQL 5.6

  1. Enable slow query log and log every query, then use Pt-index-usage tool
  2. Or use the following query (as suggested by FromDual blog post):
SELECT object_schema, object_name, index_name
  FROM performance_schema.table_io_waits_summary_by_index_usage
 WHERE index_name IS NOT NULL
   AND count_star = 0
 ORDER BY object_schema, object_name;

Q: Does the duplicate index is found on 5.6/5.7 will that causes an performance impact to the db while querying?

Duplicate keys can have negative impact on selects:

  1. MySQL can get confused and choose a wrong index
  2. Total index size can grow, which can cause MySQL to run out of RAM

Q: What is the suggested method to measure performance on queries (other than the slow query log) so as to know where to create indexes?

Slow query log is most common method. In MySQL 5.6 you can also use Performance Schema and use events_statements_summary_by_digest table.

Q: I’m not sure if this was covered in the webinar but… are there any best-practices for fulltext indexes?

That was not covered in this webinar, however, I’ve done a number of presentations regarding Full Text Indexes. For example: Creating Geo Enabled Applications with MySQL 5.6

Q: What would be the limit on index size or number of indexes you can defined per table?

There are no limits on Index size on disk, however, it will be good (performance wise) to have active indexes fit in RAM.

In InnoDB there are a number of index limitations, i.e. a table can contain a maximum of 64 secondary indexes.

Q:  If a table has two columns you would like to sum, can you have that sum indexed as a calculated index? To add to that, can that calculated index have “case when”?

Just to clarify, this is only a feature of MySQL 5.7 (not released yet).

Yes, it is documented now:

CREATE TABLE triangle (
  sidea DOUBLE,
  sideb DOUBLE,
  sidec DOUBLE AS (SQRT(sidea * sidea + sideb * sideb))
);

Q: I have noticed that you created indexes on columns like DayOfTheWeek with very low cardinality. Shouldn’t that be a bad practice normally?

Yes, you are right! Unless, you are doing queries like “select count(*) from … where DayOfTheWeek = 7” those indexes may not be very useful.

Q: I saw an article that if you don’t specify a primary key upfront mysql / innodb creates one in the background (hidden). Is it different from a primary key itself, if most of the where fields that are used not in the primary / semi primary key? And is there a way to identify the tables with the hidden primary key indexes?

The “hidden” primary key will be 6 bytes, which will also be appended (duplicated) to all secondary keys. You can create an INT primary key auto_increment, which will be smaller (if you do not plan to store more than 4 billion rows). In addition, you will not be able to use the hidden primary key in your queries.

The following query (against information_schema) can be used to find all tables without declared primary key (with “hidden” primary key):

SELECT tables.table_schema, tables.table_name, tables.table_rows
FROM information_schema.tables
LEFT JOIN (
  SELECT table_schema, table_name
  FROM information_schema.statistics
  GROUP BY table_schema, table_name, index_name
  HAVING
    SUM(
      CASE WHEN non_unique = 0 AND nullable != 'YES' THEN 1 ELSE 0 END
    ) = COUNT(*)
) puks
ON tables.table_schema = puks.table_schema AND tables.table_name = puks.table_name
WHERE puks.table_name IS NULL
AND tables.table_type = 'BASE TABLE' AND engine='InnoDB'

You may also use mysql.innodb_index_stats table to find rows with the hidden primary key:

Example:

mysql> select * from mysql.innodb_index_stats;
+---------------+------------+-----------------+---------------------+--------------+------------+-------------+-----------------------------------+
| database_name | table_name | index_name      | last_update         | stat_name    | stat_value | sample_size | stat_description                  |
+---------------+------------+-----------------+---------------------+--------------+------------+-------------+-----------------------------------+
| test          | t1         | GEN_CLUST_INDEX | 2015-08-08 20:48:23 | n_diff_pfx01 | 96         | 1           | DB_ROW_ID                         |
| test          | t1         | GEN_CLUST_INDEX | 2015-08-08 20:48:23 | n_leaf_pages | 1          | NULL        | Number of leaf pages in the index |
| test          | t1         | GEN_CLUST_INDEX | 2015-08-08 20:48:23 | size         | 1          | NULL        | Number of pages in the index      |
+---------------+------------+-----------------+---------------------+--------------+------------+-------------+-----------------------------------+

Q: You are using the alter table to create index, but how does mysql sort the data for creating the index? isn’t it uses temp table for that?

That is a very good question: the behavior of the “alter table … add index” has changed over time. As documented in Overview of Online DDL:

Historically, many DDL operations on InnoDB tables were expensive. Many ALTER TABLE operations worked by creating a new, empty table defined with the requested table options and indexes, then copying the existing rows to the new table one-by-one, updating the indexes as the rows were inserted. After all rows from the original table were copied, the old table was dropped and the copy was renamed with the name of the original table.

MySQL 5.5, and MySQL 5.1 with the InnoDB Plugin, optimized CREATE INDEX and DROP INDEX to avoid the table-copying behavior. That feature was known as Fast Index Creation

When MySQL uses “Fast Index Creation” operation it will create a set of temporary files in MySQL’s tmpdir:

To add a secondary index to an existing table, InnoDB scans the table, and sorts the rows using memory buffers and temporary files in order by the values of the secondary index key columns. The B-tree is then built in key-value order, which is more efficient than inserting rows into an index in random order.

Q: How good is InnoDB deadlocks on 5.7 comparing to 5.6 version. Is that based on parameters setup?

InnoDB deadlocks discussion is outside of the scope of this presentation. Valerii Kravchuk and Nilnandan Joshi did an excellent talk at Percona Live 2015 (slides available): Understanding Innodb Locks and Deadlocks

Q: What is the performance impact of generating a virtual column for a table having 66 Million records and generating the index. And how would you go about it? Do you have any suggestions on how to re organize indexes on the physical disk?

As MySQL 5.7 is not released yet, behavior of the virtual columns may change.  The main question here is: will it be online operations to a) add a virtual column (as this is only metadata change it should be very light operation anyway). b) add index on that virtual column. In the labs released it was not online, however this can change.

Thank you again for attending.

The post Advanced Query Tuning in MySQL 5.6 and MySQL 5.7 Webinar: Q&A appeared first on MySQL Performance Blog.

ObjectRocket’s David Murphy talks about MongoDB, Percona Live Amsterdam

Say hello to David Murphy, lead DBA and MongoDB Master at ObjectRocket (a Rackspace company). David works on sharding, tool building, very large-scale issues and high-performance MongoDB architecture. Prior to ObjectRocket he was a MySQL/NoSQL architect at Electronic Arts. David enjoys large-scale operational tool building, high performance OS and database tuning. He is also a core code contributor to MongoDB. He’ll be speaking next month at Percona Live Amsterdam, which runs Sept. 21-13. Enter promo code “BlogInterview” at registration to save €20!


Tom: David, your 3-hour tutorial is titled “Mongo Sharding from the trench: A Veterans field guide.” Did your experience in working with vast amounts of data at Rackspace give you a unique perspective, in view, that now puts you into a position to help people just getting started? Can you give a couple examples?

David: I think this has been something organically I grew into from the days of supporting Cpanel type MySQL instances to today. I have worked for a few verticals from hosts to advertising to gaming, finally entering into the platform service. The others give me a host of knowledge around how customer need systems to work, and then the number and range of workloads we see at Rackspace re-enforces this.

ObjectRocket's David Murphy talks MongoDB & Percona Live Amsterdam

ObjectRocket’s David Murphy

Many times the unique perspective comes with the scale such as someone calling up a single node to the multi-terabyte range. When they go to “shard” they can find the process that is normally very light and unnoticeable to most Mongo sharding can severally lock the metadata for an extended time. In other cases, the “balancer” might not be able to keep up with the amount of working being asked of it.

Toward the smaller end of the spectrum, having seen so many workloads from big to small. I can see similar thought processes and trends. When this happens having worked with some many of these workloads, and honestly having learned along the evolution of mongo helps me explain to clients the good, bad, and the hairy. Many times discussions come down to people not using connection pooling, non-indexed sorting, or complex operators such as $in, $nin, and more. In these cases, I can talk to people about the balance of using these concepts and when they will become bigger issues for them. My goal is to give them the enough knowledge to help determine when it is correct to use development resource to fix and issue, and when it’s manageable and that development could be better spent elsewhere.

 

Tom: The title of your tutorial also sounds like the perfect title of a book. Do you have any for one?

David: What an excellent question! I have thought about this. However, I think the goal of a book if I can find the time to do it. A working title might be “Mongo from the trenches: Surviving the minefield to get ahead”. I think the book might be broken into three sections:  “When should you use or not user Mongo”,  “Schema and Operatorators in the NoSQL world”, “Sharding”. I would do this as this could be a great mini book on its own the community really could use a level of depth similar to the MySQL 5.0 certification guides.  I liked these books as it helped someone understand all the bits of what to consider with your schema design and how it affects the application as much as the database hosts. Then in the second half more administration geared it took those same schema and design choices to help you manage them with confidence.

In the end, Mongo is a good product that works well for most people as it matures we need more and discussion. On topics such as what should you monitor, how you should predict issues, and how valuable are regular audits. Especially in an ecosystem where it’s easy to spin something up, launch it, and move on to the next project.

 

Tom: When and why would you recommend using MongoDB instead of MySQL?

David: I am glad I mentioned this is worthy of a book already, as it is such a complex topic and one that gets me very excited.

I feel there is a bit or misinformation on both sides of this field. Many in the MySQL camp of experts know when someone says they can’t get more than 1000 TPS via MySQL. 9 out of 10 times and design, not a technology issue,  the Mongo crowd love this and due to inherit sharding nature of Mongo they can sidestep these types of issues. Conversely in the Mongo camp you will hear how bad the  SQL standard is, however, omitting transactions for a moment, the same types of operations exist in MySQL and Mongo.  There are some interesting powers in the Mongo aggregation. However, SQL is more powerful and just as complex as some map reduce jobs and aggregations I have written.

As to your question, MySQL will always win in regards to repeatable reads to the database in a transaction. There is some talk of limited transactions in Mongo. However, these will likely not become global and cluster wide anytime soon if ever.  I don’t trust floats in Mongo for financials; it’s not that Mongo doesn’t do them but rather JavaScript type floats are present. Sometimes you need to store data as a 64-bit integer and do math in the app to make it a high precision float. MySQL, on the other hand, has excellent support for precision.

Another area is simply looking at the history of Mongo and MySQL.  Mongo until WiredTiger and  RocksDB were very similar to MyISAM from a locking behavior and support perspective. With the advent of the new storage system, we will-will see major leaps forward in types of flows you will want in Mongo. With the writer lock issue is gone, and locking between the systems is becoming more and more similar making deciding which much harder.

The news is not all use. However, subdocuments and array support in Mongo is amazing there are so many things  I can do in Mongo that even in bitwise SET/ENUM operators I could not do. So if you need that type of system, or you want to create a semi denormalize for of a view in the database. Mongo can do this with ease and on the fly. MySQL, on the other hand, would take careful planning and need whole tables updated.  In this regard I feel more people could use Mongo and is ability to have a versioned document schema allowing more incremental changes to documents. With new code  releases, allowing the application to read old version and “upgrade” them to the latest form. Removing a whole flurry of maintenance related pains that RDBMs have to the frustration of developers who just want to launch the new product.

The last thing I would want to say here is you need not choose, why not use both. Mongo can be very powerful for keeping a semi denormalized version of the data that is nimble to allow fast application or system updates and features. Leaving MySQL for a very specific workload that need the precision are simple are not expected to have schema changes.  I am a huge fan of keeping the transactional portions in MySQL, and the rest in Mongo. Allowing you to scale quickly up and down the build of your data needs, and more slowly change the parts that need to be 100% consistent all of the time with no room for eventual consistency.

 

Tom: What another session(s) are you most looking forward to besides your own at Percona Live Amsterdam?

David: There are a few that are near and dear to me.

Turtles all the way down: tuning Linux for database workloads” looks like a great one. It is one view I have always had, and DBA’s should be DBA’s,  SysAdmins, and Storage people rolled into one. That way they can understand the impacts of the application down to the blocks the database reads.

TokuDB internals” is another one. I have used TokuDB in MySQL and Mongo to some degree but as it has never had in-depth documentation. A topic like that is a great way to fill any gaps for experienced and new people alike.

Database Reliability Engineering” looks like a great talk from a great speaker.

As an InnoDB geek, I like the idea around “Understanding InnoDB locks: case studies.”

I see a huge amount of potential for MaxScale if anyone else is curious, “Anatomy of a Proxy Server: MaxScale Internals” should be good for R/W splits and split writing type cases.

Finally, one of my favorite people is Charity as she always is so energetic and can get to the heart of the matter. If you are not going to “Upgrade your database: without losing your data, your perf or your mind” you are missing out!

 

Tom: Thanks for speaking with me, David! Is there anything else you’d like to add: either about Rackspace or Percona Live Amsterdam?

David: In regards to Rackspace, I urge everyone to check out the Data Services group.  We handle everything from Redis to Hadoop with a goal of augmenting your groups or providing experts to help keep your uptime as high as possible. With options for dedicated hosts to platform type services, there is something that helps everyone. Rackspace is not just a cloud company but a real support company that provides amazing hardware to use, or support for other hardware location that is growing rapidly.

With Percona Amsterdam, everyone should come the group of speakers is simply amazing, I for one am excited by so many topics because they are all so compelling. Outside of that you will it hard find another a gathering of database experts with multiple technologies under their belt and who truly believe in the move to picking the right technology for the right use case.

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Percona Toolkit 2.2.15 is now available

Percona ToolkitPercona is pleased to announce the availability of Percona Toolkit 2.2.15.  Released August 28, 2015. Percona Toolkit is a collection of advanced command-line tools to perform a variety of MySQL server and system tasks that are too difficult or complex for DBAs to perform manually. Percona Toolkit, like all Percona software, is free and open source.

This release is the current GA (Generally Available) stable release in the 2.2 series. It includes multiple bug fixes as well as continued preparation for MySQL 5.7 compatibility. Full details are below. Downloads are available here and from the Percona Software Repositories.

New Features:

  • Added --max-flow-ctl option to pt-online-schema-change and pt-archiver with a value set in percent. When a Percona XtraDB Cluster node is very loaded, it sends flow control signals to the other nodes to stop sending transactions in order to catch up. When the average value of time spent in this state (in percent) exceeds the maximum provided in the option, the tool pauses until it falls below again.Default is no flow control checking.
  • Added the --sleep option for pt-online-schema-change to avoid performance problems. The option accepts float values in seconds.
  • Implemented ability to specify --check-slave-lag multiple times for pt-archiver. The following example enables lag checks for two slaves:
    pt-archiver --no-delete --where '1=1' --source h=oltp_server,D=test,t=tbl --dest h=olap_server --check-slave-lag h=slave1 --check-slave-lag h=slave2 --limit 1000 --commit-each
  • Added the --rds option to pt-kill, which makes the tool use Amazon RDS procedure calls instead of the standard MySQL kill command.

Bugs Fixed:

  • Fixed bug 1042727: pt-table-checksum doesn’t reconnect the slave $dbh
    Before, the tool would die if any slave connection was lost. Now the tool waits forever for slaves.
  • Fixed bug 1056507: pt-archiver --check-slave-lag agressiveness
    The tool now checks replication lag every 100 rows instead of every row, which significantly improves efficiency.
  • Fixed bug 1215587: Adding underscores to constraints when using pt-online-schema-change can create issues with constraint name length
    Before, multiple schema changes lead to underscores stacking up on the name of the constraint until it reached the 64 character limit. Now there is a limit of two underscores in the prefix, then the tool alternately removes or adds one underscore, attempting to make the name unique.
  • Fixed bug 1277049pt-online-schema-change can’t connect with comma in password
    For all tools, documented that commas in passwords provided on the command line must be escaped.
  • Fixed bug 1441928: Unlimited chunk size when using pt-online-schema-change with --chunk-size-limit=0 inhibits checksumming of single-nibble tables
    When comparing table size with the slave table, the tool now ignores --chunk-size-limit if it is set to zero to avoid multiplying by zero.
  • Fixed bug 1443763: Update documentation and/or implentation of pt-archiver --check-interval
    Fixed the documentation for --check-interval to reflect its correct behavior.
  • Fixed bug 1449226: pt-archiver dies with “MySQL server has gone away” when --innodb_kill_idle_transaction is set to a low value and --check-slave-lag is enabled
    The tool now sends a dummy SQL query to avoid timing out.
  • Fixed bug 1446928: pt-online-schema-change not reporting meaningful errors
    The tool now produces meaningful errors based on text from MySQL errors.
  • Fixed bug 1450499: ReadKeyMini causes pt-online-schema-change session to lock under some circumstances
    Removed ReadKeyMini, because it is no longer necessary.
  • Fixed bug 1452914: --purge and --no-delete are mutually exclusive, but still allowed to be specified together by pt-archiver
    The tool now issues an error when --purge and --no-delete are specified together.
  • Fixed bug 1455486: pt-mysql-summary is missing the --ask-pass option
    Added the --ask-pass option to the tool.
  • Fixed bug 1457573: pt-mysql-summary fails to download pt-diskstats pt-pmp pt-mext pt-align
    Added the -L option to curl and changed download address to use HTTPS.
  • Fixed bug 1462904: pt-duplicate-key-checker doesn’t support triple quote in column name
    Updated TableParser module to handle literal backticks.
  • Fixed bug 1488600: pt-stalk doesn’t check TokuDB status
    Implemented status collection similar to how it is performed for InnoDB.
  • Fixed bug 1488611: various testing bugs related to newer Perl versions

Details of the release can be found in the release notes and the 2.2.15 milestone on Launchpad. Bugs can be reported on the Percona Toolkit launchpad bug tracker.

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High-load clusters and desynchronized nodes on Percona XtraDB Cluster

There can be a lot of confusion and lack of planning in Percona XtraDB Clusters in regards to nodes becoming desynchronized for various reasons.  This can happen a few ways:

When I say “desynchronized” I mean a node that is permitted to build up a potentially large wsrep_local_recv_queue while some operation is happening.  For example a node taking a backup would set wsrep_desync=ON during the backup and potentially fall behind replication some amount.

Some of these operations may completely block Galera from applying transactions, while others may simply increase load on the server enough that it falls behind and applies at a reduced rate.

In all the cases above, flow control is NOT used while the node cannot apply transactions, but it MAY be used while the node is recovering from the operation.  For an example of this, see my last blog about IST.

If a cluster is fairly busy, then the flow control that CAN happen when the above operations catch up MAY be detrimental to performance.

Example setup

Let us take my typical 3 node cluster with workload on node1.  We are taking a blocking backup of some kind on node3 so we are executing the following steps:

  1. node3> set global wsrep_desync=ON;
  2. Node3’s “backup” starts, this starts with FLUSH TABLES WITH READ LOCK;
  3. Galera is paused on node3 and the wsrep_local_recv_queue grows some amount
  4. Node3’s “backup” finishes, finishing with UNLOCK TABLES;
  5. node3> set global wsrep_desync=OFF;

During the backup

This includes up through step 3 above.  My node1 is unaffected by the backup on node3, I can see it averaging 5-6k writesets(transactions) per second which it did before we began:

Screen Shot 2015-08-19 at 2.38.34 PM

 

node2 is also unaffected:

Screen Shot 2015-08-19 at 2.38.50 PM

but node3 is not applying and its queue is building up:

Screen Shot 2015-08-19 at 2.39.04 PM

Unlock tables, still wsrep_desync=ON

Let’s examine briefly what happens when node3 is permitted to start applying, but wsrep_desync stays enabled:

Screen Shot 2015-08-19 at 2.42.16 PM

node1’s performance is pretty much the same, node3 is not using flow control yet. However, there is a problem:

Screen Shot 2015-08-19 at 2.43.13 PM

It’s hard to notice, but node3 is NOT catching up, instead it is falling further behind!  We have potentially created a situation where node3 may never catch up.

The PXC nodes were close enough to the red-line of performance that node3 can only apply just about as fast (and somewhat slower until it heats up a bit) as new transactions are coming into node1.

This represents a serious concern in PXC capacity planning:

Nodes do not only need to be fast enough to handle normal workload, but also to catch up after maintenance operations or failures cause them to fall behind.

Experienced MySQL DBA’s will realize this isn’t all that different than Master/Slave replication.

Flow Control as a way to recovery

So here’s the trick:  if we turn off wsrep_desync on node3 now, node3 will use flow control if and only if the incoming replication exceeds node3’s apply rate.  This gives node3 a good chance of catching up, but the tradeoff is reducing write throughput of the cluster.  Let’s see what this looks like in context with all of our steps.  wsrep_desync is turned off at the peak of the replication queue size on node3, around 12:20PM:

Screen Shot 2015-08-19 at 2.47.12 PM

Screen Shot 2015-08-19 at 2.48.07 PM

So at the moment node3 starts utilizing flow control to prevent falling further behind, our write throughput (in this specific environment and workload) is reduced by approximately 1/3rd (YMMV).   The cluster will remain in this state until node3 catches up and returns to the ‘Synced’ state.  This catchup is still happening as I write this post, almost 4 hours after it started and will likely take another hour or two to complete.

I can see a more realtime representation of this by using myq_status on node1, summarizing every minute:

[root@node1 ~]# myq_status -i 1m wsrep
mycluster / node1 (idx: 1) / Galera 3.11(ra0189ab)
         Cluster  Node       Outbound      Inbound       FlowC     Conflct Gcache     Appl
    time P cnf  # stat laten msgs data que msgs data que pause snt lcf bfa   ist  idx  %ef
19:58:47 P   5  3 Sync 0.9ms 3128 2.0M   0   27 213b   0 25.4s   0   0   0 3003k  16k  62%
19:59:47 P   5  3 Sync 1.1ms 3200 2.1M   0   31 248b   0 18.8s   0   0   0 3003k  16k  62%
20:00:47 P   5  3 Sync 0.9ms 3378 2.2M  32   27 217b   0 26.0s   0   0   0 3003k  16k  62%
20:01:47 P   5  3 Sync 0.9ms 3662 2.4M  32   33 266b   0 18.9s   0   0   0 3003k  16k  62%
20:02:47 P   5  3 Sync 0.9ms 3340 2.2M  32   27 215b   0 27.2s   0   0   0 3003k  16k  62%
20:03:47 P   5  3 Sync 0.9ms 3193 2.1M   0   27 215b   0 25.6s   0   0   0 3003k  16k  62%
20:04:47 P   5  3 Sync 0.9ms 3009 1.9M  12   28 224b   0 22.8s   0   0   0 3003k  16k  62%
20:05:47 P   5  3 Sync 0.9ms 3437 2.2M   0   27 218b   0 23.9s   0   0   0 3003k  16k  62%
20:06:47 P   5  3 Sync 0.9ms 3319 2.1M   7   28 220b   0 24.2s   0   0   0 3003k  16k  62%
20:07:47 P   5  3 Sync 1.0ms 3388 2.2M  16   31 251b   0 22.6s   0   0   0 3003k  16k  62%
20:08:47 P   5  3 Sync 1.1ms 3695 2.4M  19   39 312b   0 13.9s   0   0   0 3003k  16k  62%
20:09:47 P   5  3 Sync 0.9ms 3293 2.1M   0   26 211b   0 26.2s   0   0   0 3003k  16k  62%

This reports around 20-25 seconds of flow control every minute, which is consistent with that ~1/3rd of performance reduction we see in the graphs above.

Watching node3 the same way proves it is sending the flow control (FlowC snt):

mycluster / node3 (idx: 2) / Galera 3.11(ra0189ab)
         Cluster  Node       Outbound      Inbound       FlowC     Conflct Gcache     Appl
    time P cnf  # stat laten msgs data que msgs data que pause snt lcf bfa   ist  idx  %ef
17:38:09 P   5  3 Dono 0.8ms    0   0b   0 4434 2.8M 16m 25.2s  31   0   0 18634  16k  80%
17:39:09 P   5  3 Dono 1.3ms    0   0b   1 5040 3.2M 16m 22.1s  29   0   0 37497  16k  80%
17:40:09 P   5  3 Dono 1.4ms    0   0b   0 4506 2.9M 16m 21.0s  31   0   0 16674  16k  80%
17:41:09 P   5  3 Dono 0.9ms    0   0b   0 5274 3.4M 16m 16.4s  27   0   0 22134  16k  80%
17:42:09 P   5  3 Dono 0.9ms    0   0b   0 4826 3.1M 16m 19.8s  26   0   0 16386  16k  80%
17:43:09 P   5  3 Jned 0.9ms    0   0b   0 4957 3.2M 16m 18.7s  28   0   0 83677  16k  80%
17:44:09 P   5  3 Jned 0.9ms    0   0b   0 3693 2.4M 16m 27.2s  30   0   0  131k  16k  80%
17:45:09 P   5  3 Jned 0.9ms    0   0b   0 4151 2.7M 16m 26.3s  34   0   0  185k  16k  80%
17:46:09 P   5  3 Jned 1.5ms    0   0b   0 4420 2.8M 16m 25.0s  30   0   0  245k  16k  80%
17:47:09 P   5  3 Jned 1.3ms    0   0b   1 4806 3.1M 16m 21.0s  27   0   0  310k  16k  80%

There are a lot of flow control messages (around 30) per minute.  This is a lot of ON/OFF toggles of flow control where writes are briefly delayed rather than a steady “you can’t write” for 20 seconds straight.

It also interestingly spends a long time in the Donor/Desynced state (even though wsrep_desync was turned OFF hours before) and then moves to the Joined state (this has the same meaning as during an IST).

Does it matter?

As always, it depends.

If these are web requests and suddenly the database can only handle ~66% of the traffic, that’s likely a problem, but maybe it just slows down the website somewhat.  I want to emphasize that WRITES are what is affected here.  Reads on any and all nodes should be normal (though you probably don’t want to read from node3 since it is so far behind).

If this were some queue processing that had reduced throughput, I’d expect it to possibly catch up later

This can only be answered for your application, but the takeaways for me are:

  • Don’t underestimate your capacity requirements
  • Being at the redline normally means you are well past the redline for abnormal events.
  • Plan for maintenance and failure recoveries
  • Where possible, build queuing into your workflows so diminished throughput in your architecture doesn’t generate failures.

Happy clustering!

Graphs in this post courtesy of VividCortex.

The post High-load clusters and desynchronized nodes on Percona XtraDB Cluster appeared first on MySQL Performance Blog.

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