Data is the soul of every organization. It is a necessity for practically every industry today. Yet, creating a robust data warehouse demands enormous computing ability to manage production and expeditious retrieval of data and conclusions. In extension, the company’s IT units demand to obtain new skills and foundation know-how to build and manage their BI systems. This article is all about how to replicate MySQL to Redshift. Let’s take a look at it in more detail.
Because of MySQL’s built-in vulnerabilities, many businesses replicate data to Redshift. There are 4 methods to achieve this. These are as follows:
The most manageable approach to replicate to Redshift is to export the whole MySQL data. Yet, this is also the least productive method. The first step is to export the database. For this, use the export data by utilizing MySQL’s mysqldump command. A standard mysqldump command works as follows:
$ mysqldump -h themysqlhost -u user sampledatabase sampletable1 sampletable2 --output-file dump.sql
The result of the above-fired command is not useful. The thing here is that users have to change the statement into a format proper for Redshift import. For this, the next step is to use the transform technique.
For the most reliable upload administration, transform the SQL command into the format of the tab-separated values. This can be achieved by applying the Redshift COPY command. The COPY command transforms the SQL command into the format of the tab-separated values. Then it batches uploads the data into a Redshift record. For instance, a row of data in the MySQL dump will be as follows:
mysql> INSERT INTO `myusers` (`id`, `firstname`, `lastname`, `age`) VALUES (2433, ‘Jeff’, ‘Smith’, 46),(1966,’John’,Rider’);
The next step is to import the data. In this, after transforming the MySQL command, the last action is to import it to Redshift. To execute this, just run the COPY command as given below:
COPY users FROM 's3://sample_s3_bucket/unload-samplefolder/users_' credentials 'my_access_key_no=sample_access_key;aws_secret_access_key=sample_secret_key';
The important thing to remember here is that this import and export is the most uncomplicated method to replicate to Redshift. Yet, it is not perfect for regular updates. For instance, it needs approximately half-hour to export 20 GB of data from MySQL over a 100 Mbps internet speed. It demands an extra half hour to send that database into Redshift. This implies users encounter zero connection problems through import or export, which would push users to begin the method over. A more effective approach to replicating MySQL to Redshift is the incremental SELECT and COPY method.
A COPY command is the most effective technique to store a table. Users can also insert data to the tables by utilizing INSERT commands, though it is much less effective than applying COPY. The COPY command can perceive from various data files or aggregated data streamlets concurrently. Amazon Redshift designates the workload to the batch junctions and delivers the load processes in similarity, including ordering the rows and sharing data over junction parts.
The SELECT and COPY process only refreshes the data that has evolved after the latest update. This uses considerably shorter time and bandwidth related to sending and transporting the whole dataset. SELECT and COPY allow users to sync MySQL and Redshift much more regularly.
To apply the incremental method, the MySQL table has to satisfy a few requirements:
The first step is to implement the Incremental SELECT statement, This command is used to export only the rows that have been modified from the latest update. The SELECT command is given below:
SELECT * FROM users WHERE updated_at >= ‘2021-10-24 18:00:00’;
Now, the next step is again transformation. This transformation move is identical to the import and export process. In this, again simply transform the MySQL data into the format of the tab-separated values for Redshift.
Now, the transformed MySQL TSV file holds both refreshed rows and recently inserted rows. One cannot just fire a COPY command directly to the target Redshift table. This would create the transformed rows to be replicated. The following query is displaying how to insert the rows into the destination table:
INSERT INTO users (id, ename, lastname, updated_at) SELECT id, ename, lastname, updated_at FROM sampleusers_staging a
Change data capture (CDC) is a method that catches modifications done to data in MySQL and implements it to the target Redshift table. It’s just like the incremental SELECT and COPY in that it only carries modified data, not the whole database.
In this, the very first step is to configure the MySQL config parameters to facilitate binlog. This can be done with the help of the following:
log_bin = /file_path/mysql-bin.log
MySQL’s binlog holds an organized log of each action that was executed by the database. After an original dump of the primary state of the MySQL database, the binlog is regularly run and loaded into Amazon Redshift.
By using ETL tools, users can copy or replicate data to Redshift. This can be executed in expected real-time. Just like the CDC process, such tools can handle the whole replication method and automatically draft MySQL data types into forms utilized by Redshift. Users can also synchronize various MySQL databases (and other kinds of databases too) to Redshift.
Conventional OLTP applications and advanced web applications normally perform numerous inadequate data change processes, where concurrency is important. Data interpretation and recording applications usually cover data modification services that modify many rows at once, where the foremost concern is the I/O to record massive volumes of data and hold records up-to-date. For implanting and refreshing large amounts of data (known in the business as ETL, for “extract-transform-load”), sometimes users utilize other SQL commands or outside commands, that simulate the consequences of INSERT, UPDATE, and DELETE commands.
As explained in this post, there are various techniques to replicate data from MySQL to Redshift. Techniques vary from simple to complicated, and painstakingly moderate to approaching real-time. The approach users exercise depends on various factors.