Devyn  Reilly

Devyn Reilly

1631090166

What Are JOINS in MySQL? Getting Started with JOINs

Large quantities of data are often stored in MySQL databases. Analysts and DBAs are constantly required to extract records from two or more tables based on specific requirements in order to evaluate that data quickly. This is when JOINS come in handy.

In a single query, JOINS are utilized to get data from several tables. In MySQL, JOINS aggregate data from several tables and get it as a single result. Each table in a relational database includes unique or common data, and each table is connected logically. JOINS are used to obtain data from tables that share a common field.

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What Are JOINS in MySQL? Getting Started with JOINs
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

Whitney  Durgan

Whitney Durgan

1620689340

Types of MySQL Joins | MySQL Joins [With Syntax]

Introduction

MySQLis an open-source Relational Database Management System (RDBMS) backed by Oracle. MySQL is made up of two words- “My”, the name of co-founder Michael Widenius’s daughter, and “SQL”, the abbreviation for Structured Query Language.

The word Relational in RDBMS is the fundamental on which JOINS in MySQLwork. There can be many types and numbers of tables in any Database. JOINS as the name suggests link tables together using common columns. One basic example is, Table 1 has employee details and Table 2 has project details.

If we have to check the projects an employee is involved in, we can join the tables on employee name or employee id, hence doing so will join the tables and the new virtual table (can be saved) can be accessed with all the information required.

#sql #sql joins #mysql #mysql joins

Brad  Hintz

Brad Hintz

1599302760

Apache Spark’s Join Algorithms

One of the most frequently used transformations in Apache Spark is Join operation. Joins in Apache Spark allow the developer to combine two or more data frames based on certain (sortable) keys. The syntax for writing a join operation is simple but some times what goes on behind the curtain is lost. Internally, for Joins Apache Spark proposes a couple of Algorithms and then chooses one of them. Not knowing what these internal algorithms are, and which one does spark choose might make a simple Join operation expensive.

While opting for a Join Algorithm, Spark looks at the size of the data frames involved. It considers the Join type and condition specified, and hint (if any) to finally decide upon the algorithm to use. In most of the cases, Sort Merge join and Shuffle Hash join are the two major power horses that drive the Spark SQL joins. But if spark finds the size of one of the data frames less than a certain threshold, Spark puts up Broadcast Join as it’s top contender.

Broadcast Hash Join

Looking at the Physical plan of a Join operation, a Broadcast Hash Join in Spark looks like this

Joins in Apache Spark: Broadcast Join

The above plan shows that the data frame from one of the branches broadcasts to every node containing the other data frame. In each node, Spark then performs the final Join operation. This is Spark’s per-node communication strategy.

Spark uses the Broadcast Hash Join when one of the data frame’s size is less than the threshold set in spark.sql.autoBroadcastJoinThreshold. It’s default value is 10 Mb, but can be changed using the following code

spark.conf.set("spark.sql.autoBroadcastJoinThreshold", 100 * 1024 * 1024)

This algorithm has the advantage that the other side of the join doesn’t require any shuffle. If this other side is very large, not doing the shuffle will bring notable speed-up as compared to other algorithms that would have to do the shuffle.

Broadcasting large datasets can also lead to timeout errors. A configuration spark.sql.broadcastTimeout sets the maximum time that a broadcast operation should take, past which the operation fails. The default timeout value is 5 minutes, but it can be set as follows:

spark.conf.set("spark.sql.broadcastTimeout", time_in_sec)

Sort Merge Join

If neither of the data frames can be broadcasted, then Spark resorts to Sort Merge Join. This algorithm uses the node-node communication strategy, where Spark shuffles the data across the cluster.

Sort Merge Join requires both sides of the join to have correct partitioning and order. Generally, this is ensured by** shuffle and sort** in both branches of the join as depicted below

#apache spark #scala #tech blogs #broadcast join #join opertaions #join optimization #joins in spark #shuffled hash join #sort merge join

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

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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

Whitney  Durgan

Whitney Durgan

1618911221

Setting MySQL Configuration Variables - MySQL 5.7 vs MySQL 8.0

In this article, we will explain the differences in managing the configuration variables between MySQL 5.7 and MySQL 8.0.

MySQL configuration variables are a set of server system variables used to configure the operation and behavior of the server. In this blog post, we will explain the differences in managing the configuration variables between MySQL 5.7 and MySQL 8.0.

We will explain three different ways for setting the configuration variables based on your use-case. Configuration variables that can be set at run-time are called Dynamic variables and those that need a MySQL server restart to take effect are called Non-Dynamic variables.

#mysql #mysql 5.7 #mysql server #mysql 8.0