Why We Choose a Scale-Out Database as a MySQL Alternative

We explore how one team migrated their MySQL database to another database, TiDB, that better fits their data storage needs.

Chehaoduo is an online trading platform for both new cars and personal used cars. Founded in 2015, it is now one of the largest auto trading platforms in China, valued at $9 billion in its series D round of funding last year.

In the early stages of Chehaoduo, to quickly adapt to our application development, we chose MySQL as our major database. However, as our business evolved, we were greatly troubled by the complication of MySQL sharding and schema changes. In the face of this dilemma, we found an alternative database to MySQL: TiDB, an open-source, MySQL compatible database that scales out to hold massive data.

In this post, I’ll share with you why we chose TiDB and how it empowers our application to provide better service for our customers.

#database #mysql

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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.

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Create a Scale-Out Hive Cluster With a Distributed, MySQL-Compatible Database

Hive Metastore supports various backend databases, among which MySQL is the most commonly used. However, in real-world scenarios, MySQL’s shortcoming is obvious: as metadata grows in Hive, MySQL is limited by its standalone performance and can’t deliver good performance. When individual MySQL databases form a cluster, the complexity drastically increases. In scenarios with huge amounts of metadata (for example, a single table has more than 10 million or even 100 million rows of data), MySQL is not a good choice.

We had this problem, and our migration story proves that TiDB, an open-source distributed Hybrid Transactional/Analytical Processing (HTAP) database, is a perfect solution in these scenarios.

In this post, I’ll share with you how to create a Hive cluster with TiDB as the Metastore database at the backend so that you can use TiDB to horizontally scale Hive Metastore without worrying about database capacity.

Why Use TiDB in Hive as the Metastore Database?

TiDB is a distributed SQL database built by PingCAP and its open-source community. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability. It’s a one-stop solution for both Online Transactional Processing (OLTP) and Online Analytical Processing (OLAP) workloads.

In scenarios with enormous amounts of data, due to TiDB’s distributed architecture, query performance is not limited to the capability of a single machine. When the data volume reaches the bottleneck, you can add nodes to improve TiDB’s storage capacity.

Because TiDB is compatible with the MySQL protocol, it’s easy to switch Hive’s Metastore database to TiDB. You can use TiDB as if you were using MySQL, with almost no changes:

• For the existing Hive cluster, you can use the mysqldump tool to replicate all data in MySQL to TiDB.
• You can use the metadata initialization tool that comes with Hive to create a new Hive cluster.

How to Create a Hive Cluster With TiDB

Creating a Hive cluster with TiDB involves the following steps:

• Meet component requirements
• Install a Hive cluster
• Deploy a TiDB cluster
• Configure Hive
• Initialize metadata
• Launch Metastore and test

#database #tutorial #mysql #hive #mysql database #scale out #hive cluster

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.

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

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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

System Databases in SQL Server

Introduction

In SSMS, we many of may noticed System Databases under the Database Folder. But how many of us knows its purpose?. In this article lets discuss about the System Databases in SQL Server.

System Database

Fig. 1 System Databases

There are five system databases, these databases are created while installing SQL Server.

• Master
• Model
• MSDB
• Tempdb
• Resource

Master

• This database contains all the System level Information in SQL Server. The Information in form of Meta data.
• Because of this master database, we are able to access the SQL Server (On premise SQL Server)

Model

• This database is used as a template for new databases.
• Whenever a new database is created, initially a copy of model database is what created as new database.

MSDB

• This database is where a service called SQL Server Agent stores its data.
• SQL server Agent is in charge of automation, which includes entities such as jobs, schedules, and alerts.

TempDB

• The Tempdb is where SQL Server stores temporary data such as work tables, sort space, row versioning information and etc.
• User can create their own version of temporary tables and those are stored in Tempdb.
• But this database is destroyed and recreated every time when we restart the instance of SQL Server.

Resource

• The resource database is a hidden, read only database that holds the definitions of all system objects.
• When we query system object in a database, they appear to reside in the sys schema of the local database, but in actually their definitions reside in the resource db.

#sql server #master system database #model system database #msdb system database #sql server system databases #ssms #system database #system databases in sql server #tempdb system database

Migrating from Amazon Aurora to MySQL Database Service

See how you can easily migrate your data from Amazon Aurora to MySQL Database Service. Benefit from 1100x performance increase at 1/3 the cost, right away.

You will takeaway quick steps and best practices for your database migration.

#mysql database service #mysql database #mysql