![]() ![]() However, the degree of concurrency can impact the other transactions running on the cluster. To improve load performance, you can load multiple tables concurrently. With the list ( linenumber) partition strategy and concurrent load, the approximate load time is 2 minutes, 30 seconds. Without a partition strategy, the approximate load time is 8 minutes, 20 seconds. Partitioning the lineitem table on the linenumber column and loading all partitions concurrently shows significant improvement in load time. Load from Amazon S3 supports explicit partition selection and only locks the partition where data is being loaded. If you’re using partitioned tables, consider loading partitions in parallel to improve load performance. It’s quite evident that load performance falls very sharply with small files, but doesn’t meaningfully increase with very large files. The following graph shows the import time versus file size while loading 50 million records to the lineitem table on a db.r4.4xlarge instance. For optimal performance, consider a file size between 100 MB–1 GB. Loading several small files (1–10 MB) adds overhead of file contention and impacts the load performance. Make sure that the source files in the S3 bucket aren’t too small. This might change depending on your cluster loads and instance type. Load testing was carried out when no other active transactions were running on the cluster. This post bases these observations on a series of tests loading 50 million records to the lineitem table on a db.r4.4xlarge instance (see the preceding sections for table structure and example records). This section discusses a few best practices for bulk loading large datasets from Amazon S3 to your Aurora MySQL database. Load_prefix: s3://sample-loaddata01/unload-data/file ![]() MySQL> select * from rora_s3_load_history order by load_timestamp desc limit 1\G If you’re trying this feature out for the first time, consider using the LIMIT clause for a larger table. To export data from the Aurora table to the S3 bucket, use the SELECT INTO OUTFILE S3 The following statement exports the entire table to the S3 bucket.The following screenshot displays a few records from the lineitem table. | lshipmode | varchar(10) | NO | | NULL | | | ordertotalprice | int(11) | NO | | NULL | | | extendedprice | int(11) | NO | | NULL | | ![]() | shippriority | varchar(1) | NO | | NULL | | | orderpriority | varchar(15) | NO | | NULL | | | Field | Type | Null | Key | Default | Extra | The policy must have access to the S3 bucket where the files are stored (for this post, sample-loaddata01). Create an IAM policy with the least-restricted privilege to the resources in the following code and name it aurora-s3-access-pol.Create the following required Identity and Access Management (IAM) policies and roles:.You can also use MySQL Workbench for this purpose. Launch an Amazon EC2 instance that you installed the MySQL client on.Prerequisitesīefore you get started, complete the following: This means customers of all sizes and industries can use it to store and protect any amount of data. Overview of Aurora and Amazon S3Īurora is a MySQL and PostgreSQL-compatible relational database built for the cloud, which combines the performance and availability of traditional enterprise databases with the simplicity and cost-effectiveness of open-source databases.Īmazon S3 is an object storage service that offers industry-leading scalability, data availability, security, and performance. This blog post focuses on Amazon Aurora MySQL. Export import data to Amazon S3 feature is also available for Amazon Aurora PostgreSQL. This post demonstrates how you can export and import the data from Aurora MySQL to Amazon Simple Storage Service (Amazon S3) and shares associated best practices. Aurora makes it easy to set up, operate, and scale a relational database in the cloud. Amazon Aurora is the preferred choice for OLTP workloads. You can build highly distributed applications using a multitude of purpose-built databases by decoupling complex applications into smaller pieces, which allows you to choose the right database for the right job. ![]()
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