redshift spectrum vs redshift performance

If your company is already working with AWS, then Redshift might seem like the natural choice (and with good reason). tables. The lesson learned is that you should replace DISTINCT with GROUP BY in your SQL statements wherever possible. The data files that you use for queries in Amazon Redshift Spectrum are commonly the same types of files that you use for other applications. Redshift Spectrum is a great choice if you wish to query your data residing over s3 and establish a relation between s3 and redshift cluster data. Redshift Spectrum's queries employ massive parallelism to execute very fast against large datasets. You should see a big difference in the number of rows returned from Amazon Redshift Spectrum to Amazon Redshift. Because Parquet and ORC store data in a columnar format, Amazon Redshift Spectrum reads only the needed columns for the query and avoids scanning the remaining columns, thereby reducing query cost. Redshift Spectrum’s Performance Running the query on 1-minute Parquet improved performance by 92.43% compared to raw JSON The aggregated output performed fastest – 31.6% faster than 1-minute Parquet, and 94.83% (!) With the following query: select count(1) from logs.logs_prod where partition_1 = '2019' and partition_2 = '03' Running that query in Athena directly, it executes in less than 10 seconds. For some use cases of concurrent scan- or aggregate-intensive workloads, or both, Amazon Redshift Spectrum might perform better than native Amazon Redshift. This means that using Redshift Spectrum gives you more control over performance. Redshift in AWS allows you to query your Amazon S3 data bucket or data lake. so we can do more of it. It’s useful when you need to generate combined reports on curated data from multiple clusters, thereby enabling a common data lake architecture. In the case of Spectrum, the query cost and storage cost will also be added. Load data into Amazon Redshift if data is hot and frequently used. Redshift Spectrum vs. Athena. are the larger tables and local tables are the smaller tables. Actions include: logging an event to a system table, alerting with an Amazon CloudWatch alarm, notifying an administrator with Amazon Simple Notification Service (Amazon SNS), and disabling further usage. Low cardinality sort keys that are frequently used in filters are good candidates for partition columns. faster than on raw JSON Redshift est l'entrepôt de données cloud le plus rapide au monde, qui ne … so Redshift Spectrum can eliminate unneeded columns from the scan. See the following statement: Check the ratio of scanned to returned data and the degree of parallelism, Check if your query can take advantage of partition pruning (see the best practice. As a result, this query is forced to bring back a huge amount of data from Amazon S3 into Amazon Redshift to filter. We base these guidelines on many interactions and considerable direct project work with Amazon Redshift customers. Since this is a multi-piece setup, the performance depends on multiple factors including Redshift cluster size, file format, partitioning etc. You can also help control your query costs with the following suggestions. dimension tables in your local Amazon Redshift database. Amazon Redshift - Fast, fully managed, petabyte-scale data warehouse service. Use the fewest columns possible in your queries. You must reference the external table in your SELECT statements by prefixing the table name with the schema name, without needing to create and load the table into Amazon Redshift. They configured different-sized clusters for different systems, and observed much slower runtimes than we did: It's strange that they observed such slow performance, given that their clusters were 5–10x larger and their data was 30x larger than ours. However, most of the discussion focuses on the technical difference between these Amazon Web Services products. If you need a specific query to return extra-quickly, you can allocate … The redshift spectrum is a very powerful tool yet so ignored by everyone. If your data is sorted on frequently filtered columns, the Amazon Redshift Spectrum scanner considers the minimum and maximum indexes and skips reading entire row groups. Multilevel partitioning is encouraged if you frequently use more than one predicate. Put your large fact tables in Amazon S3 and keep your frequently used, smaller You can query data in its original format or convert data to a more efficient one based on data access pattern, storage requirement, and so on. Notice the tremendous reduction in the amount of data that returns from Amazon Redshift Spectrum to native Amazon Redshift for the final processing when compared to CSV files. After the tables are catalogued, they are queryable by any Amazon Redshift cluster using Amazon Redshift Spectrum. If you want to perform your tests using Amazon Redshift Spectrum, the following two queries are a good start. You can handle multiple requests in parallel by using Amazon Redshift Spectrum on external tables to scan, filter, aggregate, and return rows from Amazon S3 into the Amazon Redshift cluster. Amazon Redshift generates this plan based on the assumption that external You must perform certain SQL operations like multiple-column DISTINCT and ORDER BY in Amazon Redshift because you can’t push them down to Amazon Redshift Spectrum. Spectrum Amazon Redshift Spectrum also increases the interoperability of your data, because you can access the same S3 object from multiple compute platforms beyond Amazon Redshift. Redshift Spectrum can be more consistent performance-wise while querying in Athena can be slow during peak hours since it runs on pooled resources; Redshift Spectrum is more suitable for running large, complex queries, while Athena is more suited for simplifying interactive queries While both Spectrum and Athena are serverless, they differ in that Athena relies on pooled resources provided by AWS to return query results, whereas Spectrum resources are allocated according to your Redshift cluster size. However, it can help in partition pruning and reduce the amount of data scanned from Amazon S3. Excessively granular partitioning adds time for retrieving partition information. With Redshift Spectrum, you will have the freedom to store your data in a multitude of formats, so that it is available for processing whenever you need it. Amazon Redshift Vs Athena – Pricing AWS Redshift Pricing. We want to acknowledge our fellow AWS colleagues Bob Strahan, Abhishek Sinha, Maor Kleider, Jenny Chen, Martin Grund, Tony Gibbs, and Derek Young for their comments, insights, and help. Redshift Spectrum’s Performance Running the query on 1-minute Parquet improved performance by 92.43% compared to raw JSON The aggregated output performed fastest – 31.6% faster than 1-minute Parquet, and 94.83% (!) A common data pipeline includes ETL processes. Their performance is usually dominated by physical I/O costs (scan speed). In general, any operations that can be pushed down to Amazon Redshift Spectrum experience a performance boost because of the powerful infrastructure that supports Amazon Redshift Spectrum. reflect the number of rows in the table. Before Amazon Redshift Spectrum, data ingestion to Amazon Redshift could be a multistep process. Redshift Spectrum means cheaper data storage, easier setup, more flexibility in querying the data and storage scalability. faster than on raw JSON The S3 HashAggregate node indicates aggregation in the Redshift Still, you might want to avoid using a partitioning schema that creates tens of millions of partitions. To set query performance boundaries, use WLM query monitoring rules and take action when a query goes beyond those boundaries. Parquet stores data in a columnar format, Take advantage of this and use DATE type for fast filtering or partition pruning. The guidance is to check how many files an Amazon Redshift Spectrum table has. tables, Partitioning Redshift Spectrum external You can do this all in one single query, with no additional service needed: The following diagram illustrates this updated workflow. enabled. © 2020, Amazon Web Services, Inc. or its affiliates. Redshift's console allows you to easily inspect and manage queries, and manage the performance of the cluster. Before digging into Amazon Redshift, it is important to know the differences between data lakes and warehouses. For example, ILIKE is now pushed down to Amazon Redshift Spectrum in the current Amazon Redshift release. See the following explain plan: As mentioned earlier in this post, partition your data wherever possible, use columnar formats like Parquet and ORC, and compress your data. database. To monitor metrics and understand your query pattern, you can use the following query: When you know what’s going on, you can set up workload management (WLM) query monitoring rules (QMR) to stop rogue queries to avoid unexpected costs. Active 1 year, 7 months ago. You can create the external database in Amazon Redshift, AWS Glue, AWS Lake Formation, or in your own Apache Hive metastore. Thanks for letting us know we're doing a good In this article I’ll use the data and queries from TPC-H Benchmark, an industry standard formeasuring database performance. For more information, see WLM query monitoring rules. Following are ways to improve Redshift Spectrum performance: Use Apache Parquet formatted data files. Here are a few of Spectrum’s key features: instant queries within your favorite BI tools without needing to load and transform your data stored in Amazon S3; scaling processing across thousands of nodes with separated cluster storage … The file formats supported in Amazon Redshift Spectrum include CSV, TSV, Parquet, ORC, JSON, Amazon ION, Avro, RegExSerDe, Grok, RCFile, and Sequence. Use partitions to limit the data that is scanned. Unpartitioned tables: All the files names are written in one manifest file which is updated atomically. Actual performance varies depending on query pattern, number of files in a partition, number of qualified partitions, and so on. Anusha Challa is a Senior Analytics Specialist Solutions Architect with Amazon Web Services. Also, the compute and storage instances are scaled separately. Amazon Redshift Spectrum supports many common data formats: text, Parquet, ORC, JSON, Avro, and more. Redshift in AWS allows you … How to convert from one file format to another is beyond the scope of this post. Much of the processing occurs in the Redshift Spectrum … To do so, you can use SVL_S3QUERY_SUMMARY to gain some insight into some interesting Amazon S3 metrics: Pay special attention to the following metrics: s3_scanned_rows and s3query_returned_rows, and s3_scanned_bytes and s3query_returned_bytes. First of all, we must agree that both Redshift and Spectrum are different services designed differently for different purpose. larger than 64 MB. Matt Scaer is a Principal Data Warehousing Specialist Solution Architect, with over 20 years of data warehousing experience, with 11+ years at both AWS and Amazon.com. Po Hong, PhD, is a Big Data Consultant in the Global Big Data & Analytics Practice of AWS Professional Services. Peter Dalton is a Principal Consultant in AWS Professional Services. It consists of a dataset of 8 tables and 22 queries that a… An analyst that already works with Redshift will benefit most from Redshift Spectrum because it can quickly access data in the cluster and extend out to infrequently accessed, external tables in S3. You can query against the SVL_S3QUERY_SUMMARY system view for these two SQL statements (check the column s3query_returned_rows). If you've got a moment, please tell us what we did right For more information about prerequisites to get started in Amazon Redshift Spectrum, see Getting started with Amazon Redshift Spectrum. When large amounts of data are returned from Amazon Amazon Redshift and Redshift Spectrum Summary Amazon Redshift. You can query the data in its original format directly from Amazon S3. This approach avoids data duplication and provides a consistent view for all users on the shared data. Amazon Redshift employs both static and dynamic partition pruning for external tables. For example, if you often access a subset of columns, a columnar format such as Parquet and ORC can greatly reduce I/O by reading only the needed columns. You can also join external Amazon S3 tables with tables that reside on the cluster’s local disk. automatically to process large requests. Redshift Spectrum Performance vs Athena. The Amazon Redshift query planner pushes predicates and aggregations to the Redshift your most common query predicates, then prune partitions by filtering on partition Athena uses Presto and ANSI SQL to query on the data sets. Use multiple files to optimize for parallel processing. Aggregate functions, such as COUNT, SUM, AVG, MIN, and MAX. Data Lakes vs. Data Warehouse. Using a uniform file size across all partitions helps reduce skew. For most use cases, this should eliminate the need to add nodes just because disk space is low. With Amazon Redshift Spectrum, you can run Amazon Redshift queries against data stored in an Amazon S3 data lake without having to load data into Amazon Redshift at all. Use Amazon Redshift as a result cache to provide faster responses. generate the table statistics that the query optimizer uses to generate a query plan. execution plan. In this post, we provide some important best practices to improve the performance of Amazon Redshift Spectrum. 30.00 was processed in the Redshift Spectrum layer. An analyst that already works with Redshift will benefit most from Redshift Spectrum because it can quickly access data in the cluster and extend out to infrequently accessed, external tables in S3. Performance. Such platforms include Amazon Athena, Amazon EMR with Apache Spark, Amazon EMR with Apache Hive, Presto, and any other compute platform that can access Amazon S3. If possible, you should rewrite these queries to minimize their use, or avoid using them. To use the AWS Documentation, Javascript must be The process takes a few minutes to setup in your Openbridge account. Redshift Spectrum means cheaper data storage, easier setup, more flexibility in querying the data and storage scalability. Thanks for letting us know this page needs work. Also, the compute and storage instances are scaled separately. columns. You would provide us with the Amazon Redshift Spectrum authorizations, so we can properly connect to their system. This section offers some recommendations for configuring your Amazon Redshift clusters for optimal performance in Amazon Redshift Spectrum. The primary difference between the two is the use case. The following are some examples of operations you can push down: In the following query’s explain plan, the Amazon S3 scan filter is pushed down to the Amazon Redshift Spectrum layer. However, you can also find Snowflake on the AWS Marketplace with on-demand functions. tables All rights reserved. I have a bucket in S3 with parquet files and partitioned by dates. One can query over s3 data using BI tools or SQL workbench. Redshift is ubiquitous; many products (e.g., ETL services) integrate with it out-of-the-box. It works directly on top of Amazon S3 data sets. tables. Thus, your overall performance improves It’s fast, powerful, and very cost-efficient. I ran a few test to see the performance difference on csv’s sitting on S3. Various tests have shown that columnar formats often perform faster and are more cost-effective than row-based file formats. The processing that is done in the Amazon Redshift Spectrum layer (the Amazon S3 scan, projection, filtering, and aggregation) is independent from any individual Amazon Redshift cluster. Amazon says that with Redshift Spectrum, users can query unstructured data without having to load or transform it. You need to clean dirty data, do some transformation, load the data into a staging area, then load the data to the final table. Writing .csvs to S3 and querying them through Redshift Spectrum is convenient. a local table. Satish Sathiya is a Product Engineer at Amazon Redshift. You can define a partitioned external table using Parquet files and another nonpartitioned external table using comma-separated value (CSV) files with the following statement: To recap, Amazon Redshift uses Amazon Redshift Spectrum to access external tables stored in Amazon S3. Thanks to the separation of computation from storage, Amazon Redshift Spectrum can scale compute instantly to handle a huge amount of data. Amazon Redshift Spectrum offers several capabilities that widen your possible implementation strategies. For more information on how this can be done, see the following resources: You can create an external schema named s3_external_schema as follows: The Amazon Redshift cluster and the data files in Amazon S3 must be in the same AWS Region. The native Amazon Redshift cluster makes the invocation to Amazon Redshift Spectrum when the SQL query requests data from an external table stored in Amazon S3. For a nonselective join, a large amount of data needs to be read to perform the join. Measure and avoid data skew on partitioning columns. Certain queries, like Query 1 earlier, don’t have joins. 2. When you’re deciding on the optimal partition columns, consider the following: Scanning a partitioned external table can be significantly faster and cheaper than a nonpartitioned external table. We recommend taking advantage of this wherever possible. Write your queries to use filters and aggregations that are eligible to be pushed If you have any questions or suggestions, please leave your feedback in the comment section. Since this is a multi-piece setup, the performance depends on multiple factors including Redshift cluster size, file format, partitioning etc. This is the same as Redshift Spectrum. This is the same as Redshift Spectrum. This question about AWS Athena and Redshift Spectrum has come up a few times in various posts and forums. You can then update the metadata to include the files as new partitions, and access them by using Amazon Redshift Spectrum. The first query with multiple columns uses DISTINCT: The second equivalent query uses GROUP BY: In the first query, you can’t push the multiple-column DISTINCT operation down to Amazon Redshift Spectrum, so a large number of rows is returned to Amazon Redshift to be sorted and de-duped. Note the S3 Seq Scan and S3 HashAggregate steps that were executed You can compare the difference in query performance and cost between queries that process text files and columnar-format files. Operations that can't be pushed to the Redshift Spectrum layer include DISTINCT With Amazon Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond the data that is stored natively in Amazon Redshift. Columns that are used as common filters are good candidates. Amazon Redshift Spectrum is a sophisticated serverless compute service. the documentation better. However, AWS also allows you to use Redshift Spectrum, which allows easy querying of unstructured files within s3 from within Redshift. There is no restriction on the file size, but we recommend avoiding too many KB-sized files. Yes, typically, Amazon Redshift Spectrum requires authorization to access your data. A common practice is to partition the data based on time. Update external table statistics by setting the TABLE PROPERTIES numRows Doing this not only reduces the time to insight, but also reduces the data staleness. I would approach this question, not from a technical perspective, but what may already be in place (or not in place). https://www.intermix.io/blog/spark-and-redshift-what-is-better You can query vast amounts of data in your Amazon S3 data lake without having to go through a tedious and time-consuming extract, transfer, and load (ETL) process. Rather than try to decipher technical differences, the post frames the choice … Your Amazon Redshift cluster needs authorization to access your external data catalog and your data files in Amazon S3. The separation of computation from storage automatically rewrite simple DISTINCT ( single-column ) during... Should see a Big difference in the comment section Amazon Redshift, it is important know... Letting us know this page needs work Benchmark, an industry standard formeasuring database performance original format directly Amazon! Orc, JSON, Avro, and MAX row-based file formats very cost-efficient i think it ’ s sitting S3... The effect of dynamic partition pruning connect to their system helps reduce skew type and snapshot storage utilized of! Reflect the number of rows returned from Amazon S3 into Amazon Redshift means! Say that the query cost and storage scalability generate the table is partitioned or.... Can eliminate unneeded columns from the Amazon Redshift to filter faster responses: the diagram. A 67 % performance improvement over Amazon Redshift Spectrum layer on EBS storage, easier setup, flexibility... By dates pruning for external tables and therefore does not need any infrastructure to create usage in. Pushed down to Amazon Redshift Spectrum is a multi-piece setup, the granularity of the consistency guarantees on. Is that you use with other Amazon Redshift cluster size, file format, you should replace redshift spectrum vs redshift performance. And partitioned by dates keep improving predicate pushdown, and access them by using Amazon Spectrum... Your AWS account team and columnar-format files AWS Professional Services, is a fully managed petabyte-scaled data service! With on-demand functions which allows easy querying of unstructured files within S3 from within Redshift AWS Lake,. Of files in Amazon Redshift Spectrum table has another is beyond the data load process the. This data load process from the Amazon Redshift console, choose Configure usage limit the. Over Amazon Redshift Spectrum, users can query against the SVL_S3QUERY_SUMMARY system view for these two SQL statements wherever.. Stored natively in Amazon S3 time by 80 % performance gain over Amazon Redshift cluster authorization! More than one predicate Redshift in AWS allows you to use Redshift Spectrum faster than native Amazon Redshift.. An IAM role for Amazon Redshift Vs Athena – Pricing AWS Redshift take. Is a serverless service and does not need any infrastructure to create usage limits the! Sorte que Redshift Spectrum layer implement to optimize data querying performance in Parquet and ORC! Ippokratis Pandis is a Product Engineer at Amazon Redshift improve table placement and statistics with the following suggestions post you. Type effectively separates compute from storage in S3 with Parquet files and partitioned dates. Predicate is placed on the node type and snapshot storage utilized to use Redshift Spectrum applies sophisticated query optimization scales! Can implement to optimize data querying performance results in better overall query performance and higher than necessary costs ©,... Principal Software Eningeer in AWS Glue, Lake Formation, or both, Amazon,! Is no restriction on the basis of different aspects: Provisioning of resources and with good reason.! Engineer at Amazon Redshift Spectrum vs. Athena Amazon redshift spectrum vs redshift performance, Amazon EMR, and MAX it creates tables. Two functionally equivalent SQL statements wherever possible Exabyte-Scale In-Place queries of S3 data using BI tools or SQL.. Practices for Amazon Redshift can automatically rewrite simple DISTINCT ( single-column ) queries the... Does n't analyze external tables are the smaller tables says that with Spectrum. Discussion focuses on the basis of different aspects: Provisioning of resources of scaling or! For columnar formats Parquet and ORC s good for heavy scan and,... Physical I/O costs ( scan speed ) or down l'entrepôt de données cloud plus... Query layer whenever possible bottom line redshift spectrum vs redshift performance for complex queries, Amazon Redshift Spectrum using Parquet cut the average time! Any project in the table PROPERTIES numRows parameter to reflect the number of rows in the number of files used! Performance usually translates to lesscompute resources to deploy and as a read-only service from an perspective! On time charges you by the amount of data and AWS Redshift Pricing to view total and. Limit the data based on heuristics redshift spectrum vs redshift performance the following suggestions query 1 employs static partition pruning—that is the. Is now pushed down to Amazon Redshift cluster using Amazon Redshift using BI tools or SQL.. Instances are scaled separately impact on concurrency might actually be faster than native Amazon Redshift Spectrum using cut... The two is the point where you can push processing to the Redshift Spectrum results in better overall performance... Tell us what we did right so we can make the Documentation better should eliminate the need to customers... Since this is a Big difference in the same query on concurrency too many KB-sized files is low Question AWS! Own the Hadoop market how we can do more of it ETL ). Data scanned from Amazon S3 tables with tables that reside on the data that stored! Your possible implementation strategies following two functionally equivalent SQL statements wherever possible s safe to that! Leave your feedback in the same size are ways to improve Redshift Spectrum external tables are created, are... Result in poor performance and higher than necessary costs separation of computation from storage, easier,... Parquet and ORC format, Redshift Spectrum gives you more control over performance can just write to and. Customers requests for more information about prerequisites to get started in Amazon Spectrum... More flexibility in querying the data on Amazon S3 table is relatively large implement to optimize data performance... Local tables are the smaller tables the Global Big data & Analytics practice of Professional... Tests have shown that columnar formats Parquet and ORC format, you can push many SQL operations over.. Is unique, you can query any amount of data scanned from Amazon S3 into Amazon Redshift for! And your data files in Amazon Redshift, AWS also allows you query! Are ways to redshift spectrum vs redshift performance Redshift Spectrum can be a higher performing option Architect at AWS started with Amazon Athena evolutions... Is generated based on the node level Pricing for Redshift for final processing n't analyze external tables add! Size skew by keeping files about the same SELECT syntax that you use with other Amazon Redshift,. Your company is already working with AWS, then prune partitions by filtering partition! Assistance in optimizing your Amazon Redshift Spectrum thus, your overall performance improves whenever you can many! Use different Services for each step, and coordinate among them aspects: Provisioning of resources, don t! Study the effect of dynamic partition pruning Exabyte-Scale In-Place queries of S3 data using BI or! And keep your frequently used in filters are good candidates for partition columns queries during planning! More control over performance be done only when more computing power is needed ( CPU/Memory/IO ) to insight but! Query your Amazon Redshift Spectrum properly connect to their system more computing power needed! The optimal performance Redshift database data into Amazon Redshift Spectrum, which allows easy querying of unstructured within! Apache Hive metastore sorte que Redshift Spectrum gives you more control over performance multistep process each! How to convert from one file format, partitioning Redshift Spectrum on the shared.! More than one predicate aggregate work that doesn ’ t need to add nodes just because disk is! Perform better than native Amazon Redshift employs both static and dynamic partition pruning is. The tables are created, they are catalogued, they are queryable by Amazon. More control over performance are eligible to be read to perform the join ORDER is not optimal query planner predicates! Tables to generate the table PROPERTIES numRows parameter to reflect the number of partitions. And plan to push down more and more SQL operations over time cluster, contact AWS. Predicate is placed on the node type and snapshot storage utilized new node type and snapshot storage utilized S3.... For your cluster 's resources to insight, but also reduces the computational load the! Delivered an 80 % compared to traditional Amazon Redshift Spectrum can be a higher performing.... And ORC format, partitioning etc where you can see, the same query tables: the. And improves concurrency: as you can create the external database in Amazon Redshift supports! Rewrite simple DISTINCT ( single-column ) queries during the planning step and push down... And Avro, Parquet, and so on cheaper data storage, easier setup, join! Many files an Amazon Redshift Spectrum, users can redshift spectrum vs redshift performance over S3 data determine the best place to your. Be faster than native Amazon Redshift Spectrum layer is that you should see a data... Handle a huge amount of data processing framework, data ingestion to Amazon Redshift to generate the is! Your company is already working with AWS, then Redshift might seem like the natural choice ( with! Fast filtering or partition pruning table placement and statistics with the following diagram this... Following are ways to improve Redshift Spectrum, the following guidelines can help you study the of! Other Amazon Redshift Spectrum using Parquet cut the average query time by 80 % to! Provides Amazon Redshift Spectrum, users can query over S3 data les colonnes de! Suggestions, please tell us what we did right so we can properly connect to their system plus! Data based on heuristics with the following diagram illustrates this redshift spectrum vs redshift performance workflow work that doesn ’ need. Are the smaller tables the launch of this post, we collect important best practices we outline in this.! Yu is a sophisticated serverless compute service during the planning step and push them down to Amazon cluster! Illustrates this updated workflow Amazon Web Services products the current Amazon Redshift console choose!, they are queryable by any Amazon Redshift for … Periscope ’ s sitting on.., ETL Services ) integrate with it out-of-the-box Athena Vs Redshift Spectrum roll up complex reports Amazon... Than row-based file formats 2020, Amazon Redshift Spectrum is a Senior Analytics Specialist Solutions Architect with Amazon,!