The metadata is adding about 12 MB per file which after compressing with parquet + snappy ends up in 10–50 MB size. To correspond with this optimization, Openbridge will split each incoming file into smaller ones of roughly 200 MB. Amazon recommends breaking large files into many smaller files of equal size (no larger than 64 MB) to evenly distribute the workload. To leverage MPP benefits, place the data files in a separate folder for each table, and make sure you keep it the right size. This is called massively parallel processing (MPP) and allows you to more quickly 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 Google 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. However, if you want to access the whole row by ID, columnar storage would be suboptimal, so you may want to run some tests.Īmazon Redshift Spectrum allows to run queries on S3 data without having to set up servers, define clusters, or do any maintenance of the system. 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. As a best practice to improve performance and lower costs, Amazon suggests using columnar data formats such as Apache Parquet. Use Optimized Data FormatsĪmazon Redshift Spectrum supports the following formats AVRO, PARQUET, TEXTFILE, SEQUENCEFILE, RCFILE, RegexSerDe, ORC, Grok, CSV, Ion, and JSON. Openbridge does this step for you automatically - schema and tables are created based on a 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 the 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.
#AWS EMR VS S3 COPY LOG FILES TO REDSHIFT HOW TO#
It also provides suggestions on how to augment Amazon Redshift performance and optimization efforts. This article covers a few tips when using Amazon Redshift Spectrum for interactive queries. However, if you are a current Redshift user and want to explore using Spectrum, read on! Increasingly, Athena is being used as the backbone for serverless data analytics stacks. If you are not an existing Redshift customer, Athena should be a consideration for you. In April 2017 Amazon introduced Redshift Spectrum, an interactive query service to enable Redshift customers to query directly from Amazon S3 without the need to go through time-consuming ETL workflows.Īmazon also offers another interactive query service, Amazon Athena which might also be a consideration. How to get more value from your investments to enhance Amazon Redshift performance
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