The document discusses MySQL optimization and indexing principles. It provides examples of queries with and without indexes and uses the EXPLAIN statement to analyze query performance. Indexing types for MyISAM and InnoDB storage engines are explained. Considerations for when indexes can and cannot be used are also covered. The document is presented by someone who works with MySQL and offers their contact information for questions.
15 Ways to Kill Your Mysql Application Performanceguest9912e5
Jay is the North American Community Relations Manager at MySQL. Author of Pro MySQL, Jay has also written articles for Linux Magazine and regularly assists software developers in identifying how to make the most effective use of MySQL. He has given sessions on performance tuning at the MySQL Users Conference, RedHat Summit, NY PHP Conference, OSCON and Ohio LinuxFest, among others.In his abundant free time, when not being pestered by his two needy cats and two noisy dogs, he daydreams in PHP code and ponders the ramifications of __clone().
AdminCamp2019 - We love Domino V10 - 15 neue Domino-Admin-FeaturesChristoph Adler
Domino V10 wurde mit einem ganzen Rucksack an neuen Features ausgeliefert, die das Leben von Administratoren zu einem Kinderspiel machen. In diesem Vortrag teilen wir mit Ihnen alles, was wir über unsere 15 neuen Favoriten wissen und lieben - von der lang erwarteten ODS53 bis zu brandneuen Angeboten wie Domino General Query Facility (DGQF), Deletion Logging und viel mehr. Sie erfahren, wie Sie das Beste aus den neuen Features herausholen können und was durch praktische Kundenbeispiele nachgewiesen wird.
IBM THINK 2019 - Self-Service Cloud Data Management with SQL Torsten Steinbach
SQL is a powerful language to express data transformations. But did you know that you can also use IBM Cloud SQL to convert data between various data formats and layouts on disks? In this session, you will see the full power of using SQL Query to move and transform your cloud data in an entirely self-service fashion. You can specify any data format, layout or partitioning with a simple SQL statement. See how you can move and transform terabytes of data in the cloud in a very scalable fashion and still being charged only for the individual SQL movement and transformation jobs without having standing costs.
MySQL for business developer - Titouan BENOITTitouan BENOIT
MySQL introduction for business developer.
In this presentation you will learn to:
– Make basics queries to search data into MySQL databases
– Extract data from MySQL databases
– Use PHPMyAdmin
– Compute statistics on data (count, average, …)
Ingesting Data at Blazing Speed Using Apache OrcDataWorks Summit
Big SQL is a SQL engine for Hadoop that excels at performance and scalability at high concurrency. Big SQL complements and integrates with Apache Hive for both data and metadata. An architecture that separates compute from storage allows Big SQL to support multiple open data formats natively. Until recently, Parquet provided a significant performance advantage over other data formats for SQL on Hadoop. The landscape changed when ORC became a top level Apache project independent from Hive. Gone were the days of reading ORC files using slow, single-row-at-a-time Hive Serdes. The new vectorized APIs in the Apache ORC libraries make it possible to ingest ORC data at blazing speed. This talk is about the journey leading to ORC taking the crown of best performing data format for Big SQL away from Parquet. We'll have a look under the hood at the architecture of Big SQL ORC readers, and how to tune them. We'll share lessons learned in walking the fine line between maximizing performance at scale and avoiding dreaded Java OOMs . You'll learn the techniques that SQL engines use for fast data ingestion, so that you can leverage the full potential of Apache ORC in any application.
Speaker:
Gustavo Arocena, Big Data Architect, IBM
Join Postgres experts Bruce Momjian and Marc Linster as they preview everything new in Postgres 12. You don’t want to miss this!
Highlights include:
- New compatibility features
- PostgreSQL: Table access methods
- Partitioning Improvements
15 New Domino Admin Features Sure to Spark a Lasting Love Affair with Domino ...Christoph Adler
Domino 10 shipped jam-packed with new features that will make administrators' lives a breeze. In this talk, we'll share everything we know and love about our 15 new favorites—from the long-awaited NSF size limit boost, to brand-new gems like Domino General Query Facility (DGQF), deletion logging and more. You'll learn how to get the most out of all of them, proven through practical customer examples. You'll walk away from this fast-paced, in-depth session with a solid understanding of the new way to administer Domino 10, as well as a hands-on guide to properly put these great features to use!
15 Ways to Kill Your Mysql Application Performanceguest9912e5
Jay is the North American Community Relations Manager at MySQL. Author of Pro MySQL, Jay has also written articles for Linux Magazine and regularly assists software developers in identifying how to make the most effective use of MySQL. He has given sessions on performance tuning at the MySQL Users Conference, RedHat Summit, NY PHP Conference, OSCON and Ohio LinuxFest, among others.In his abundant free time, when not being pestered by his two needy cats and two noisy dogs, he daydreams in PHP code and ponders the ramifications of __clone().
AdminCamp2019 - We love Domino V10 - 15 neue Domino-Admin-FeaturesChristoph Adler
Domino V10 wurde mit einem ganzen Rucksack an neuen Features ausgeliefert, die das Leben von Administratoren zu einem Kinderspiel machen. In diesem Vortrag teilen wir mit Ihnen alles, was wir über unsere 15 neuen Favoriten wissen und lieben - von der lang erwarteten ODS53 bis zu brandneuen Angeboten wie Domino General Query Facility (DGQF), Deletion Logging und viel mehr. Sie erfahren, wie Sie das Beste aus den neuen Features herausholen können und was durch praktische Kundenbeispiele nachgewiesen wird.
IBM THINK 2019 - Self-Service Cloud Data Management with SQL Torsten Steinbach
SQL is a powerful language to express data transformations. But did you know that you can also use IBM Cloud SQL to convert data between various data formats and layouts on disks? In this session, you will see the full power of using SQL Query to move and transform your cloud data in an entirely self-service fashion. You can specify any data format, layout or partitioning with a simple SQL statement. See how you can move and transform terabytes of data in the cloud in a very scalable fashion and still being charged only for the individual SQL movement and transformation jobs without having standing costs.
MySQL for business developer - Titouan BENOITTitouan BENOIT
MySQL introduction for business developer.
In this presentation you will learn to:
– Make basics queries to search data into MySQL databases
– Extract data from MySQL databases
– Use PHPMyAdmin
– Compute statistics on data (count, average, …)
Ingesting Data at Blazing Speed Using Apache OrcDataWorks Summit
Big SQL is a SQL engine for Hadoop that excels at performance and scalability at high concurrency. Big SQL complements and integrates with Apache Hive for both data and metadata. An architecture that separates compute from storage allows Big SQL to support multiple open data formats natively. Until recently, Parquet provided a significant performance advantage over other data formats for SQL on Hadoop. The landscape changed when ORC became a top level Apache project independent from Hive. Gone were the days of reading ORC files using slow, single-row-at-a-time Hive Serdes. The new vectorized APIs in the Apache ORC libraries make it possible to ingest ORC data at blazing speed. This talk is about the journey leading to ORC taking the crown of best performing data format for Big SQL away from Parquet. We'll have a look under the hood at the architecture of Big SQL ORC readers, and how to tune them. We'll share lessons learned in walking the fine line between maximizing performance at scale and avoiding dreaded Java OOMs . You'll learn the techniques that SQL engines use for fast data ingestion, so that you can leverage the full potential of Apache ORC in any application.
Speaker:
Gustavo Arocena, Big Data Architect, IBM
Join Postgres experts Bruce Momjian and Marc Linster as they preview everything new in Postgres 12. You don’t want to miss this!
Highlights include:
- New compatibility features
- PostgreSQL: Table access methods
- Partitioning Improvements
15 New Domino Admin Features Sure to Spark a Lasting Love Affair with Domino ...Christoph Adler
Domino 10 shipped jam-packed with new features that will make administrators' lives a breeze. In this talk, we'll share everything we know and love about our 15 new favorites—from the long-awaited NSF size limit boost, to brand-new gems like Domino General Query Facility (DGQF), deletion logging and more. You'll learn how to get the most out of all of them, proven through practical customer examples. You'll walk away from this fast-paced, in-depth session with a solid understanding of the new way to administer Domino 10, as well as a hands-on guide to properly put these great features to use!
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
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Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
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1. MySQL Optimization
Ha noi php day 2009
vanchinh@gmail.com
19/12/2009 Ha Noi - 2009 1
2. Content
Introduction
Example
Indexing Principles
Considerations
Explain
General discussion
19/12/2009 Ha Noi - 2009 2
3. Introduction
Ha noi php day 2009
vanchinh@gmail.com
19/12/2009 Ha Noi - 2009 3
4. Who am i
I working in NaisCorp (socbay.com)
I’ve been using MySQL personally for
2 years and professionally for over 1 years.
Contact info:
Personal: vanchinh@gmail.com
work: chinhnv@socbay.com
Web: http://ffx.socbay.com
19/12/2009 Ha Noi - 2009 4
5. Example
Ha noi php day 2009
vanchinh@gmail.com
19/12/2009 Ha Noi - 2009 5
6. Example
SELECT * FROM news WHERE cate_id =1
ORDER BY createdate DESC LIMIT 0,10;
SELECT * FROM news WHERE cate_id =1 or
cate_id=2 ORDER BY createdate DESC
LIMIT 0,10;
SELECT * FROM news WHERE id <
12345678 ORDER BY createdate DESC LIMIT
0, 10
19/12/2009 Ha Noi - 2009 6
7. Example
Query Parsing and Optimizing
Query
Parse
Thread 1
Thread 2
Thread 3 Optimize
Thread 4
MyISAM Key Buffer
Engine API
MyISAM
…
Thread 5
Storage
Execute (ha_myisam)
(fetch) *.MYD, *.MYI
Thread n
(update) InnoDB Key Buffer
InnoDB
(ha_innodb) ibdataN
Results
Group By
Having
Order By
Group By and Order By can be done with indexes instead.
19/12/2009 Ha Noi - 2009 7
8. Example
Query Parsing and Optimizing
All parsing and optimizing steps are the same for all
storage engines
All ORDER BY / GROUP BY steps are the same
The only difference for a given storage engine is in
actually fetching the rows
Some types of indexes don’t exist on some table
types: e.g. FULLTEXT and SPATIAL indexes don’t
exist for InnoDB
19/12/2009 Ha Noi - 2009 8
9. Example
Explain SELECT * FROM news WHERE
cate_id =1 ORDER BY createdate DESC
LIMIT 0,10;
Run time: 0.9724s
19/12/2009 Ha Noi - 2009 9
10. Example
WHERE cate_id =1 Order By createdate Limit 0,10
2.000.000 2.000.000
10
19/12/2009 Ha Noi - 2009 10
11. Indexing Principles
Ha noi php day 2009
vanchinh@gmail.com
19/12/2009 Ha Noi - 2009 11
12. Optimization – Indexing Principles
Indexing principles
Single-column index
Multi-column index
Table news
19/12/2009 Ha Noi - 2009 12
13. Optimization – Indexing Principles
Single index
Table news
Multi index
19/12/2009 Ha Noi - 2009 13
14. Optimization – Indexing Principles
Basic Index Principles
Only one index can be used, per table, per query
Index definitions must match your real-world use
You don’t have to do anything special to maintain indexes
after creation
Creating/Dropping Indexes
CREATE INDEX cate ON news (cate_id)
DROP INDEX cate ON news;
ALTER TABLE news ADD INDEX cate
(cate_id);
ALTER TABLE news DROP INDEX cate;
19/12/2009 Ha Noi - 2009 14
15. Optimization – Indexing Principles
CREATE INDEX cate_date ON news (cate_id,
createdate)
Explain SELECT * FROM news WHERE
cate_id =1 ORDER BY createdate DESC
LIMIT 0,10
Run time: 0.0007 s
19/12/2009 Ha Noi - 2009 15
16. Optimization – Indexing Principles
WHERE cate_id =1 Order By createdate Limit 0,10
2.000.000 2.000.000
10
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17. Optimization – Indexing Principles
Explain SELECT * FROM news WHERE
cate_id =1 or cate_id=2 ORDER BY createdate
DESC LIMIT 0,10
Run time: 1.0067S
19/12/2009 Ha Noi - 2009 17
18. Optimization – Indexing Principles
Cate_id=1 Order by Limit 0,10
1.000.000
Cate_id=2 3.000.000 10
2.000.000
19/12/2009 Ha Noi - 2009 18
19. Optimization – Indexing Principles
SELECT news.* FROM (
(SELECT id FROM news WHERE cate_id=1
ORDER BY createdate LIMIT 0,10)
UNION
(SELECT id FROM news WHERE cate_id=2
ORDER BY createdate LIMIT 0,10)
) as temp, news where news.id=temp.id ORDER
BY createdate DESC LIMIT 0,10;
19/12/2009 Ha Noi - 2009 19
27. Optimization – Considerations
When can indexes be used?
Simple PRIMARY KEY lookups
PRIMARY KEY(i) :: WHERE i = 5
Secondary KEY (index) lookups
INDEX(i) :: WHERE i = 7
Prefix lookups
INDEX(s) :: WHERE s LIKE “foo%”
INDEX(i, j) :: WHERE i = 5
Joins – index either table
19/12/2009 Ha Noi - 2009 27
28. Optimization – Considerations
When can’t indexes be used?
LIKE that starts with a wildcard
INDEX(s) :: WHERE s LIKE “%foo”
Bitwise operations
INDEX(i) :: WHERE i & 4
Non-prefix lookups
INDEX(i, j) :: WHERE j = 5
Using a function containing column ref
INDEX(s) :: WHERE foo(s) = “FOO”
INDEX(dt) :: WHERE unix_timestamp(dt) = 1
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29. Optimization – Explain
Explain SELECT * FROM news WHERE
cate_id = 218103808 ORDER BY id DESC
LIMIT 0,10;
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30. Optimization – Explain
Fields in EXPLAIN
id – (4.1+) The ID number of this SELECT
select_type – (4.1+) The type of this SELECT
table – The name of the table in question
type – The type of reading done on this table (system,
const, eq_ref, ref, ref_or_null, index_merge,
unique_subquery, index_subquery, range,
index, ALL)
possible_keys – The keys which were examined for
possible use
key – The key that was finally chosen for use
19/12/2009 Ha Noi - 2009 30
31. Optimization – Explain
Fields in EXPLAIN
key_len – The length of the part of the key that
was finally chosen for use
ref – Row reference; What is used to look up
row values from the index
rows – The approximate number of rows which
must be examined from this table using the chosen
optimizations
Extra – Any extra comments the server wanted
to add about this optimization path (free form)
19/12/2009 Ha Noi - 2009 31
32. General Discussion
Got any questions for me?
Ha noi php day 2009
vanchinh@gmail.com
19/12/2009 Ha Noi - 2009 32