Postgres export to parquet


  • Export table from Aurora PostgreSQL to Amazon S3
  • AWS RDS Postgres Export to S3: 5 Easy Steps
  • Data transfer
  • I will walk you through two approaches that you can use to export the data. The post also covers the performance and scaling challenges when exporting the table using AWS Glue. The writer instance is running on db. The table is Gb in size and is non-partitioned. Click Next and add tags if needed before moving to Review page. On the review page add the Role name, confirm the Trusted entities and click on create role.

    You can write the data in S3 in text, csv or binary format. The XX represents the numbers. In this case the database is running in a private subnet. Then test the connection and make sure it is successful. Few things to note while creating the AWS Glue connection — As the database is running in private subnet, you will need to choose private subnet within your VPC when creating the Glue connection. The private subnet that you choose should be part of route table which has NAT Gateway attached.

    The NAT Gateway should be attached to a public subnet. The difference between public and private subnet is that the instances in the public subnet can send outbound traffic directly to the Internet whereas private subnet can access the Internet by using a network address translation NAT gateway that resides in the public subnet.

    Add a new AWS Glue job. On the next page choose connections, which in this case will be the connection that you created earlier as it will be required by this job. The enableUpdateCatalog set to True indicate that the Data Catalog is to be updated during the job run as the new partitions are created. To create a new table in Glue catalog specify the database and new table name using setCatalogInfo along with enableUpdateCatalog and updateBehavior parameter set. The job ran with 10 workers of G.

    An error occurred while calling o If you watch the Cloudwatch metric, only 1 worker is active at a given point in time. Well, increasing the DPU would mean more memory, more threads, more disk space. This mean G. Even with G. No space left on device. With Glue 2. The bucket needs to be in the same region where the job is being executed.

    Read the table in parallel — To do so you can use either hashfield or hashexpression and hashpartitions parameter in JDBC connection options. From the Glue Document — hashfield — Set hashfield to the name of a column in the JDBC table to be used to divide the data into partitions.

    For best results, this column should have an even distribution of values to spread the data between partitions. This column can be of any data type.

    AWS Glue generates non-overlapping queries that run in parallel to read the data partitioned by this column. A simple expression is the name of any numeric column in the table. If this property is not set, the default value is 7. Optimize the memory for driver and executor process. By default the driver and executor process use 1Gb of memory. Given that we are using G. We will set the memory configurations spark. The Memory and CPU profile of the driver and executor process shows all threads being used.

    Also you will notice the data shuffle across executors. As we had set —write-shuffle-files-to-s3 : true, Spark used the Amazon S3 bucket for writing the shuffle data. The Glue job wrote the file in glueparquet format to Amazon S3. We explored how can we decouple the shuffle storage from the workers by using Amazon S3 to store the shuffle data. We updated the default memory configuration for Spark driver and executor for better performance.

    We also explored various datasink parameters which indicated that the AWS Glue Data Catalog is to be updated during the job run. Using all these mechanism we were able to optimize the AWS Glue job and run the job successfully to export the data to Amazon S3.

    References —.

    Image Source PostgreSQL has become the go-to Open-Source relational database for various startups and enterprise developers, primarily used to power mobile applications and leading businesses. It also manages time-consuming and complex administrative tasks like PostgreSQL software upgrades, backups for disaster recovery, replication for high availability and read throughput, and storage management.

    The general purpose storage is aimed at providing cost-effective storage for medium-sized or small workloads. Amazon RDS lets you provision additional storage on the fly keeping your growing storage requirements in mind. These parameters offer fine-tuning and granular control of your PostgreSQL database. This makes them a natural fit for production database workloads. You can do this for read-heavy database workloads. This can be done within your specified retention period of up to 35 days.

    You can also carry out user-initiated backups of your DB instance. These full database backups will be stored in Amazon RDS until deleted manually.

    Introduction to Amazon S3 Image Source Amazon Simple Storage Service S3 works as an object storage offering that supports industry-grade data availability, scalability, security, and performance. Customers of all sizes can leverage it to store and protect their data for various use cases such as mobile applications, websites, Big Data Analytics, IoT devices, and data lakes to name a few.

    It also offers easy-to-use management features so you can organize your data and configure finely-tuned access controls to meet your specific business needs and compliance requirements. Image Source Here are a few benefits of Amazon S3: Cost-Effective Storage Classes: You can save costs without sacrificing performance by storing data across the S3 Storage Classes, which provides support for different data access levels at corresponding cost-effective rates.

    S3 Storage Class Analysis can be used to discover data that should be moved to a lower-cost storage class based on access patterns. Highly Supported Cloud Storage Service: You can store and protect your data in Amazon S3 by working with a partner from the AWS Partner Network , the largest community of technology and consulting cloud service providers.

    It recognizes the migration partners that transfer data to Amazon S3 and storage partners that offer integrated solutions for primary storage, backups, archives, and disaster recovery. Data and Access Controls: Amazon S3 offers robust capabilities to manage access, cost, data replication, and replication.

    With Amazon S3 Access Points you can easily manage data access with specific permissions for your applications using a shared data set. Amazon S3 Replication helps you manage data replication within the region or to other regions.

    Hevo with its minimal learning curve can be set up in just a few minutes allowing the users to load data without having to compromise performance.

    Its strong integration with umpteenth sources provides users with the flexibility to bring in data of different kinds, in a smooth fashion without having to code a single line. Check out some of the cool features of Hevo: Completely Automated: The Hevo platform can be set up in just a few minutes and requires minimal maintenance. Real-Time Data Transfer: Hevo provides real-time data migration, so you can have analysis-ready data always. Live Monitoring: Hevo allows you to monitor the data flow so you can check where your data is at a particular point in time.

    You can try Hevo for free by signing up for a day free trial.

    Introduction to Amazon S3 Image Source Amazon Simple Storage Service S3 works as an object storage offering that supports industry-grade data availability, scalability, security, and performance. Customers of all sizes can leverage it to store and protect their data for various use cases such as mobile applications, websites, Big Data Analytics, IoT devices, and data lakes to name a few.

    It also offers easy-to-use management features so you can organize your data and configure finely-tuned access controls to meet your specific business needs and compliance requirements. Image Source Here are a few benefits of Amazon S3: Cost-Effective Storage Classes: You can save costs without sacrificing performance by storing data across the S3 Storage Classes, which provides support for different data access levels at corresponding cost-effective rates.

    S3 Storage Class Analysis can be used to discover data that should be moved to a lower-cost storage class based on access patterns. Highly Supported Cloud Storage Service: You can store and protect your data in Amazon S3 by working with a partner from the AWS Partner Networkthe largest community of technology and consulting cloud service providers.

    It recognizes the migration partners that transfer data to Amazon S3 and storage partners that offer integrated solutions for primary storage, backups, archives, and disaster recovery. Data and Access Controls: Amazon S3 offers robust capabilities to manage access, cost, data replication, and replication. With Amazon S3 Access Points you can easily manage data access with specific permissions for your applications using a shared data set.

    Amazon S3 Replication helps you manage data replication within the region or to other regions. Hevo with its minimal learning curve can be set up in just a few minutes allowing the users to load data without having to compromise performance.

    Its strong integration with umpteenth sources provides users with the flexibility to bring in data of different kinds, in a smooth fashion without having to code a single line.

    Export table from Aurora PostgreSQL to Amazon S3

    Check out some of the cool features of Hevo: Completely Automated: The Hevo platform can be set up in just a few minutes and requires minimal maintenance. Note: avoid changing data in tables you have selected to be exported while the exporting is in progress. In the end you will see status message: You can import data from CSV file s directly into your database table s. Select a table s to which you want to import data.

    AWS RDS Postgres Export to S3: 5 Easy Steps

    You need to set a column in the CSV file for each database table column. You can skip columns the value will be set to NULL in the target table column.

    You can set constant values for the table column if there is no source column for it in the CSV. Set options for loading data in the database. These options may affect the loading's performance: About the replacing method option, you can read here. Review which file s and to which table s you will import. You can also save all your settings as a task in this step: Press finish.

    Data transfer

    You can keep working with your database during the export process as the data loading will be performed in the background. Note: avoid changing data in tables you have selected to be imported while the import is in progress. In the end you will see the status message:.


    Postgres export to parquet