AWS Glue Demo - Part 2 Creating RedShift Cluster, Security Group and VPC Endpoint Whenever we used decimal in Redshift Spectrum and in Spark, we kept getting errors, such as: S3 Query Exception (Fetch). Then we realized that we were unnecessarily scanning a full day’s worth of data every minute. AWS has been using PartiQL since it became production-ready last year, and has shown up in Redshift Spectrum and DynamoDB, for example. Next, we compared the same three configurations with a complex query. The Glue Data Catalog is used for schema management. What is Amazon Redshift Spectrum? Redshift Spectrum is simply the ability to query data stored in S3 using your Redshift cluster. Our customers could see how their campaigns performed faster than with other solutions, and react sooner to the ever-changing media supply pricing and availability. Since Glue provides data cataloging, if you want to move high volume data, you can move data to S3 and leverage features of Redshift Spectrum from Redshift client. The most effective method to generate the Parquet files is to: With this new process, we had to give more attention to validating the data before we sent it to Kinesis Firehose, because a single corrupted record in a partition fails queries on that partition. AWS recommends using compressed columnar formats such … Setting things up Users, roles and policies. Add the Parquet data to S3 by updating the table partitions. To balance cost and analytics performance, we looked for a way to store large amounts of less-frequently analyzed data at a lower cost. Comparing the amount of data scanned when using CSV/GZIP and Parquet, the difference was also significant: Because we pay only for the data scanned by Redshift Spectrum, the cost saving of using Parquet is evident and substantial. This is the reason you see billing defined as float in Spectrum and double in the Spark code. The next challenge is, the AWS generate useractivitylog file is not in a proper structure. If you are done using your cluster, please think about decommissioning it to avoid having to pay for unused resources. But finally, with the help of AWS Support, we generated the working pattern. Our costs are now lower, and our users get fast results even for large complex queries. What is the performance difference between Amazon Redshift and Redshift Spectrum on simple and complex queries? This file contains all the SQL queries that are executed on our RedShift cluster. Whether you’re using Athena or Spectrum, performance will be heavily dependent on optimizing the S3 storage layer. Today, we will explore querying the data from a data lake in S3 using Redshift Spectrum. If on the other hand you want to integrate wit existing redshift tables, do lots of joins or aggregates go with Redshift Spectrum. You pay only for the queries you perform and only for the data scanned per query. The only way to structure unstructured data is to know the pattern and tell your database server how to retrieve the data with proper column names. We had to experiment with a few floating-point formats until we found that the only combination that worked was to define the column as double in the Spark code and float in Spectrum. Here are a few words about float, decimal, and double. Amazon Redshift cluster with 28 DC1.large nodes. Using the Parquet data format, Redshift Spectrum delivered an 80% performance improvement over Amazon Redshift. '2020-05-22T03:00:14Z UTC [ db=dev user=rdsdb pid=91025 userid=1 xid=11809754 ]', #extract the content from gzip and write to a new file, #read lines from the new file and repalce all new lines, r'(\'\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}Z UTC)', 'arn:aws:iam::123456789012:role/MySpectrumRole', %{TIMESTAMP_ISO8601:timestamp} %{TZ:timezone}, [ db=%{DATA:db} user=%{DATA:user} pid=%{DATA:pid} userid=%{DATA:userid} xid=%{DATA:xid}, 'org.apache.hadoop.mapred.TextInputFormat', 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'. All rights reserved. RedShift IAM role to Access S3 and Glue catalog. Redshift Spectrum queries employ massive parallelism to execute very fast against large datasets. What was surprising was that using Parquet data format in Redshift Spectrum significantly beat ‘traditional’ Amazon Redshift performance. It can push many compute-intensive tasks, such as predicate filtering and aggregation, down to the Redshift Spectrum layer, so that queries use much less of your cluster’s processing capacity. Lastly, since Redshift Spectrum distributes queries across potentially thousands of nodes, they are not affected by other queries, providing much more stable performance and unlimited concurrency. So we can directly use this file for further analysis. It’s fast, powerful, and very cost-efficient. The appropriate AWS Identity and Access Management (IAM) permissions for Amazon Redshift Spectrum and AWS Glue to access Amazon S3 buckets. Various Methods of Loading Data to Redshift . Two advantages here, still you can use the same table with Athena or use Redshift Spectrum to query this. AWS Glue is a great option. Athena works directly with the table metadata stored on the Glue Data Catalog while in the case of Redshift Spectrum you need to configure external tables as per each schema of the Glue Data Catalog. However, it is not supported out-of-the-box by Kinesis Firehose, so you need to implement your own ETL. Properly partitioning the data improves performance significantly and reduces query times. You put the data in an S3 bucket, and the schema catalog tells Redshift what’s what. When we started three years ago, we would offload data from each server to S3 and then perform a periodic copy command from S3 to Amazon Redshift. First, we tested a simple query aggregating billing data across a month: We ran the same query seven times and measured the response times (red marking the longest time and green the shortest time): For simple queries, Amazon Redshift performed better than Redshift Spectrum, as we thought, because the data is local to Amazon Redshift. RedShift User Activity Log In Spectrum With Glue Grok. For example, you can partition your data by date and hour to run time-based queries, and also have another set partitioned by user_id and date to run user-based queries. AWS Glue is a fully managed, cloud-native, AWS service for performing extract, transform and load operations across a wide range of data sources and destinations. The CloudFormation template also deploys the AWS Glue job HudiMoRCompactionJob. When using Redshift Spectrum, external tables need to be configured per each Glue Data Catalog schema. … The redshift spectrum is a very powerful tool yet so ignored by everyone. We wanted to know how it would compare to Amazon Redshift, so we looked at two key questions: During the migration phase, we had our dataset stored in Amazon Redshift and S3 as CSV/GZIP and as Parquet file formats. File 'https://s3-external-1.amazonaws.com/nuviad-temp/events/2017-08-01/hour=2/part-00017-48ae5b6b-906e-4875-8cde-bc36c0c6d0ca.c000.snappy.parquet has an incompatible Parquet schema for column 's3://nuviad-events/events.lat'. 6) We will learn to develop reports and dashboards, with a powerpoint like slideshow feature, and mobile support, without building any report server, by using Serverless Amazon QuickSight Reporting Engines. Amazon EMR goes far beyond just running SQL queries. RedShift user activity log(useractivitylog) will be pushed from RedShift to our S3 bucket on every 1hr internal. Amazon Redshift Spectrum . Redshift Spectrum uses the same query engine as Amazon Redshift. Make sure that you validate your data before scanning it with Redshift Spectrum. Scaling Redshift Spectrum is a simple process. At NUVIAD, we’ve been using Amazon Redshift as our main data warehouse solution for more than 3 years. Being an experienced entrepreneur, Rafi believes in practical-programming and fast adaptation of new technologies to achieve a significant market advantage. To execute the pipe for MoR storage type instead of CoW storage type, delete the CloudFormation … In this post, I explain the reasons why we extended Amazon Redshift with Redshift Spectrum as our modern data warehouse. I cover how our data growth and the need to balance cost and performance led us to adopt Redshift Spectrum. This use case makes sense for those organizations that already have a significant exposure to using Redshift as their primary data warehouse. To use an AWS Glue Data Catalog with Redshift Spectrum, you might need to change your IAM policies. For example: We were extremely pleased with using Amazon Redshift as our core data warehouse for over three years. I also share key performance metrics in our environment, and discuss the additional AWS services that provide a scalable and fast environment, with data available for immediate querying by our growing user base. While this is now a viable option, we kept the same collection process that worked flawlessly and efficiently for three years. Over the past three years, our customer base grew significantly and so did our data. Using Amazon Redshift Spectrum, you can efficiently query and retrieve structured and semistructured data from files in Amazon S3 without having to load the data into Amazon Redshift tables. One of the biggest benefits of using Redshift Spectrum (or Athena for that matter) is that you don’t need to keep nodes up and running all the time. This folder is connected to a small Redshift Spectrum table where the data is being processed without needing to scan a much larger dataset. 5) We will learn to bridge the data warehouse and data lake using Serverless Amazon Redshift Spectrum Engine built on the top of Amazon Redshift platform. Using this approach, the crawler creates the table entry in the external catalog on the user’s behalf after it determines the column data types. Our development stack is based on Node.js, which is well-suited for high-speed, light servers that need to process a huge number of transactions. RedShift subnets should have Glue Endpoint or Nat Gateway or Internet gateway. Voila, thats it. The benefits were immediately evident. In this post, I am going to discuss how we can create ETL pipelines using AWS Glue. The lack of Parquet modules for Node.js required us to implement an AWS Glue/Amazon EMR process to effectively migrate data from CSV to Parquet. We are done now, Lets do a sample query. Athena is designed to work directly with table metadata stored in the Glue Data Catalog. Even I tried to change a few things, but no luck. Use Amazon Redshift Spectrum for ad hoc processing ... Redshift with AWS Glue. Note. As mentioned earlier, a single corrupt entry in a partition can fail queries running against this partition, especially when using Parquet, which is harder to edit than a simple CSV file. With 7 years of experience in the AdTech industry and 15 years in leading technology companies, Rafi Ton is the founder and CEO of NUVIAD. We would rather save directly to Parquet, but we couldn’t find an effective way to do it. The benefits of Parquet are substantial. The solution is to store Timestamp as string and cast the type to Timestamp in the query. When we started using Redshift Spectrum, we saw our Amazon Redshift costs jump by hundreds of dollars per day. This time, Redshift Spectrum using Parquet cut the average query time by 80% compared to traditional Amazon Redshift! With Redshift Spectrum, we pay for the data scanned in each query. For more information, see And it’ll remove all the new line characters and upload them back to the bucket with a different location. Furthermore, Redshift Spectrum showed high consistency in execution time with a smaller difference between the slowest run and the fastest run. I mean if you have a long query then that particular query having a new line character. While Redshift Spectrum is great for running queries against data in Amazon Redshift and S3, it really isn’t a fit for the types of use cases that enterprises typically ask from processing frameworks like Amazon EMR. Each instance sends events that are eventually loaded into Amazon Redshift. Redshift Spectrum excels when running complex queries. You can use the same python code to run it on EC2 instance as well. 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