Devyn  Reilly

Devyn Reilly

1626849827

Indexing MySQL for High-Performance

MySQL engineers call them the cornerstone of any high-performance database: indexes are frequently used to quickly find rows matching a query.

If you have ever worked with MySQL, Percona Server, or MariaDB, you have probably wondered how you can improve the performance of your database instances. If you have sought out advice on this subject, you have likely heard about indexes.

Indexes in MySQL can be categorized into a few types:

  1. Balanced tree (B-Tree) indexes — the most frequently used type of index. This index type can be used together with search queries that use the =, >, >=, <, <= and BETWEEN keywords, also with LIKE queries.
  2. Spatial (R-Tree) indexes — can be used together with MySQL geometric data types to index geographical objects.
  3. Hash indexes — usually used only with queries that use the = or <=> search operators. Very fast but can be used only when the MEMORY storage engine is in use.
  4. Covering indexes — indexes that cover all of the columns required for a query to complete.
  5. Clustered indexes — such indexes store row data. Usually PRIMARY KEYs or, if they do not exist, UNIQUE indexes.
  6. Multicolumn (composite) indexes — indexes that are created on multiple columns.
  7. Prefix indexes — such indexes allow you only to index a prefix of a column. As such indexes do not index the full value of a column, they are frequently used to save space.

#mysql

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Indexing MySQL for High-Performance
Joe  Hoppe

Joe Hoppe

1595905879

Best MySQL DigitalOcean Performance – ScaleGrid vs. DigitalOcean Managed Databases

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MySQL is the all-time number one open source database in the world, and a staple in RDBMS space. DigitalOcean is quickly building its reputation as the developers cloud by providing an affordable, flexible and easy to use cloud platform for developers to work with. MySQL on DigitalOcean is a natural fit, but what’s the best way to deploy your cloud database? In this post, we are going to compare the top two providers, DigitalOcean Managed Databases for MySQL vs. ScaleGrid MySQL hosting on DigitalOcean.

At a glance – TLDR
ScaleGrid Blog - At a glance overview - 1st pointCompare Throughput
ScaleGrid averages almost 40% higher throughput over DigitalOcean for MySQL, with up to 46% higher throughput in write-intensive workloads. Read now

ScaleGrid Blog - At a glance overview - 2nd pointCompare Latency
On average, ScaleGrid achieves almost 30% lower latency over DigitalOcean for the same deployment configurations. Read now

ScaleGrid Blog - At a glance overview - 3rd pointCompare Pricing
ScaleGrid provides 30% more storage on average vs. DigitalOcean for MySQL at the same affordable price. Read now

MySQL DigitalOcean Performance Benchmark
In this benchmark, we compare equivalent plan sizes between ScaleGrid MySQL on DigitalOcean and DigitalOcean Managed Databases for MySQL. We are going to use a common, popular plan size using the below configurations for this performance benchmark:

Comparison Overview
ScaleGridDigitalOceanInstance TypeMedium: 4 vCPUsMedium: 4 vCPUsMySQL Version8.0.208.0.20RAM8GB8GBSSD140GB115GBDeployment TypeStandaloneStandaloneRegionSF03SF03SupportIncludedBusiness-level support included with account sizes over $500/monthMonthly Price$120$120

As you can see above, ScaleGrid and DigitalOcean offer the same plan configurations across this plan size, apart from SSD where ScaleGrid provides over 20% more storage for the same price.

To ensure the most accurate results in our performance tests, we run the benchmark four times for each comparison to find the average performance across throughput and latency over read-intensive workloads, balanced workloads, and write-intensive workloads.

Throughput
In this benchmark, we measure MySQL throughput in terms of queries per second (QPS) to measure our query efficiency. To quickly summarize the results, we display read-intensive, write-intensive and balanced workload averages below for 150 threads for ScaleGrid vs. DigitalOcean MySQL:

ScaleGrid MySQL vs DigitalOcean Managed Databases - Throughput Performance Graph

For the common 150 thread comparison, ScaleGrid averages almost 40% higher throughput over DigitalOcean for MySQL, with up to 46% higher throughput in write-intensive workloads.

#cloud #database #developer #digital ocean #mysql #performance #scalegrid #95th percentile latency #balanced workloads #developers cloud #digitalocean droplet #digitalocean managed databases #digitalocean performance #digitalocean pricing #higher throughput #latency benchmark #lower latency #mysql benchmark setup #mysql client threads #mysql configuration #mysql digitalocean #mysql latency #mysql on digitalocean #mysql throughput #performance benchmark #queries per second #read-intensive #scalegrid mysql #scalegrid vs. digitalocean #throughput benchmark #write-intensive

Loma  Baumbach

Loma Baumbach

1595781840

Exploring MySQL Binlog Server - Ripple

MySQL does not limit the number of slaves that you can connect to the master server in a replication topology. However, as the number of slaves increases, they will have a toll on the master resources because the binary logs will need to be served to different slaves working at different speeds. If the data churn on the master is high, the serving of binary logs alone could saturate the network interface of the master.

A classic solution for this problem is to deploy a binlog server – an intermediate proxy server that sits between the master and its slaves. The binlog server is set up as a slave to the master, and in turn, acts as a master to the original set of slaves. It receives binary log events from the master, does not apply these events, but serves them to all the other slaves. This way, the load on the master is tremendously reduced, and at the same time, the binlog server serves the binlogs more efficiently to slaves since it does not have to do any other database server processing.

MySQL Binlog Server Deployment Diagram - ScaleGrid Blog

Ripple is an open source binlog server developed by Pavel Ivanov. A blog post from Percona, titled MySQL Ripple: The First Impression of a MySQL Binlog Server, gives a very good introduction to deploying and using Ripple. I had an opportunity to explore Ripple in some more detail and wanted to share my observations through this post.

1. Support for GTID based replication

Ripple supports only GTID mode, and not file and position-based replication. If your master is running in non-GTID mode, you will get this error from Ripple:

Failed to read packet: Got error reading packet from server: The replication sender thread cannot start in AUTO_POSITION mode: this server has GTID_MODE = OFF instead of ON.

You can specify Server_id and UUID for the ripple server using the cmd line options: -ripple_server_id and -ripple_server_uuid

Both are optional parameters, and if not specified, Ripple will use the default server_id=112211 and uuid will be auto generated.

2. Connecting to the master using replication user and password

While connecting to the master, you can specify the replication user and password using the command line options:

-ripple_master_user and -ripple_master_password

3. Connection endpoint for the Ripple server

You can use the command line options -ripple_server_ports and -ripple_server_address to specify the connection end points for the Ripple server. Ensure to specify the network accessible hostname or IP address of your Ripple server as the -rippple_server_address. Otherwise, by default, Ripple will bind to localhost and hence you will not be able to connect to it remotely.

4. Setting up slaves to the Ripple server

You can use the CHANGE MASTER TO command to connect your slaves to replicate from the Ripple server.

To ensure that Ripple can authenticate the password that you use to connect to it, you need to start Ripple by specifying the option -ripple_server_password_hash

For example, if you start the ripple server with the command:

rippled -ripple_datadir=./binlog_server -ripple_master_address= <master ip> -ripple_master_port=3306 -ripple_master_user=repl -ripple_master_password='password' -ripple_server_ports=15000 -ripple_server_address='172.31.23.201' -ripple_server_password_hash='EF8C75CB6E99A0732D2DE207DAEF65D555BDFB8E'

you can use the following CHANGE MASTER TO command to connect from the slave:

CHANGE MASTER TO master_host='172.31.23.201', master_port=15000, master_password=’XpKWeZRNH5#satCI’, master_user=’rep’

Note that the password hash specified for the Ripple server corresponds to the text password used in the CHANGE MASTER TO command. Currently, Ripple does not authenticate based on the usernames and accepts any non-empty username as long as the password matches.

Exploring MySQL Binlog Server - Ripple

CLICK TO TWEET

5. Ripple server management

It’s possible to monitor and manage the Ripple server using the MySQL protocol from any standard MySQL client. There are a limited set of commands that are supported which you can see directly in the source code on the mysql-ripple GitHub page.

Some of the useful commands are:

  • SELECT @@global.gtid_executed; – To see the GTID SET of the Ripple server based on its downloaded binary logs.
  • STOP SLAVE; – To disconnect the Ripple server from the master.
  • START SLAVE; – To connect the Ripple server to the master.

#cloud #database #developer #high availability #mysql #performance #binary logs #gtid replication #mysql binlog #mysql protocol #mysql ripple #mysql server #parallel threads #proxy server #replication topology #ripple server

Whitney  Durgan

Whitney Durgan

1625908542

Indexing MySQL For High Performance: A High-level Overview

Some MySQL engineers might call them the cornerstone of improving performance in MySQL: indexes are data structures that are frequently used to quickly find rows matching a given query.

If you have ever worked with MySQL, Percona Server, or MariaDB, you have probably wondered how you can improve the performance of your database instances. If you have sought out advice on this subject, you have likely heard about indexes.

Indexes in MySQL can be categorized into a few types:

  1. Balanced tree (B-Tree) indexes - the most frequently used type of index. This index type can be used together with search queries that use the =, >, >=, <, <= and BETWEEN keywords, also with LIKE queries.
  2. Spatial (R-Tree) indexes - can be used together with MySQL geometric data types to index geographical objects.
  3. Hash indexes - usually used only with queries that use the = or <=> search operators. Very fast but can be used only when the MEMORY storage engine is in use.
  4. Covering indexes - indexes that cover all of the columns required for a query to complete.
  5. Clustered indexes - such indexes store row data. Usually PRIMARY KEYs or, if they do not exist, UNIQUE indexes.
  6. Multicolumn (composite) indexes - indexes that are created on multiple columns.
  7. Prefix indexes - such indexes allow you only to index a prefix of a column. As such indexes do not index the full value of a column, they are frequently used to save space.

#mysql #indexing

Devyn  Reilly

Devyn Reilly

1620609771

Create Index in MySQL: MySQL Index Tutorial [2021]

A Data Scientist or a programmer will have to work with large quantities of data and needs to be able to deal with it efficiently for faster execution. They also need to know how the data is organised and the fastest methods to easily access the particular data needed.

Since MySQL is a relational database management system that has applications in various sectors such as web databases, e-commerce applications, social media etc. data scientists need to know all the techniques related to MySQL, one of which is indexes. The article describes how to create Index in MySQL.

#index in mysql #mysql

Loma  Baumbach

Loma Baumbach

1595774031

ScaleGrid DigitalOcean Support for MySQL, PostgreSQL and Redis™

PALO ALTO, Calif., June 9, 2020 – ScaleGrid, a leading Database-as-a-Service (DBaaS) provider, has just announced support for their MySQLPostgreSQL and Redis™ solutions on DigitalOcean. This launch is in addition to their current DigitalOcean offering for MongoDB® database, the only DBaaS to support this database on DigitalOcean.

MySQL and PostgreSQL are the top two open source relational databases in the world, and Redis is the top key-value database. These databases are a natural fit for the developer market that has gravitated towards DigitalOcean since its launch just nine years ago in 2011. The open source model is not only popular with the developer market, but also enterprise companies looking to modernize their infrastructure and reduce spend.  DigitalOcean instance costs are also over 28% less expensive than AWS, and over 26% less than Azure, providing significant savings for companies who are struggling in this global climate.

ScaleGrid’s MySQL, PostgreSQL and Redis™ solutions on DigitalOcean are competitively priced starting at just $15/GB, the same as DigitalOcean’s Managed Database solution, but offer on average 30% more storage for the same price. Additionally, ScaleGrid offers several competitive advantages such as full superuser access, custom master-slave configurations, and advanced slow query analysis and monitoring capabilities through their sophisticated platform. To compare more features, check out their ScaleGrid vs. DigitalOcean MySQLScaleGrid vs. DigitalOcean PostgreSQL and ScaleGrid vs. DigitalOcean Redis™ pages.

#cloud #database #developer #digital ocean #mysql #postgresql #redis #scalegrid #advanced performance #database infrastructure #dbaas on digitalocean #digitalocean customers #digitalocean instance costs #digitalocean managed databases #high performance ssd #mysql digitalocean #postgresql digitalocean #redis digitalocean #scalegrid digitalocean #scalegrid vs. digitalocean