This is called massively parallel processing (MPP) and allows you to faster run complex queries on large amounts of data. To query external data, Redshift Spectrum uses multiple instances to scan files. Preparing Files for Massively Parallel Processing Openbridge defaults to using Snappy with Apache Parquet as it’s a trade-off between the amount of CPU utilized for processing files and the decrease in S3 storage/IO used. Compressed files are recognized by extensions.
However, to improve query return speed and performance, it is recommended to compress data files. Data CompressionĪmazon Redshift Spectrum allows you to run queries on S3 data without having to set up servers, define clusters, or do any maintenance of the system.
However, if you want to access the whole row by ID, columnar storage would be suboptimal, so you may want to run some tests. In order to benefit from this optimization, you have to query for the fewest columns possible. This is not possible with row-based formats like CSV or JSON. This also minimizes the amount of data transferred from Amazon S3 through Redshift by selecting only the columns you need. This can be done by using columnar formats like Parquet. Since Amazon Redshift Spectrum charges you per query and for the amount of data scanned from S3, it is advisable to scan only the data you need. Optimized Data FormatsĪmazon Redshift Spectrum supports the following formats: AVRO, PARQUET, TEXTFILE, SEQUENCEFILE, RCFILE, RegexSerDe, ORC, Grok, CSV, Ion, and JSON.Īs a best practice to improve performance and lower costs, Amazon suggests using columnar data formats such as Apache Parquet. Openbridge has a service that creates schema and tables automatically based on user’s storage configuration and processed files’ structure. To run Redshift Spectrum queries, the database user must have permission to create temporary tables in the database. You also need to provide authorization to access your external Athena data catalog, which can be done through an IAM console. Make sure that the data files in S3 and the Redshift cluster are in the same AWS region. Note, external tables are read-only, and won’t allow you to perform insert, update, or delete operations. You can use Amazon Athena data catalog or Amazon EMR as a “metastore” in which to create an external schema. Setting up Amazon Redshift Spectrum requires creating an external schema and tables. This article covers what is important to know when adopting Amazon Redshift Spectrum for interactive queries and how to automate certain processes to improve performance and lower query costs. The service allows to avoid time-consuming ETL workflows and run queries directly against the data stored in Amazon S3. It returns a row: spectrum,table,"",s3://parquet/account/A/5/20/.ql.io.parquet.MapredParquetInputFormat.ql.io.parquet.MapredParquetOutputFormat.ql.io. Redshift Spectrum, an interactive query service for Redshift customers, was introduced in April 2017. Where tablename='table' and schemaname='spectrum'Īnd values='' It is an hourly job, and it takes 50 minutes to insert the 30 folders data into Redshift tables. So I don't know how to improve the process speed.
And I had 168 files of few kb before trying to merge them in a single one of around 1 mb.