Category: Aws glue partition keys

If you've got a moment, please tell us what we did right so we can do more of it. Thanks for letting us know this page needs work. We're sorry we let you down. If you've got a moment, please tell us how we can make the documentation better. The Partition API describes data types and operations used to work with partitions. PartitionListComposingSpec Structure.

Values — An array of UTF-8 strings. DatabaseName — UTF-8 string, not less than 1 or more than bytes long, matching the Single-line string pattern. TableName — UTF-8 string, not less than 1 or more than bytes long, matching the Single-line string pattern.

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CreationTime — Timestamp. LastAccessTime — Timestamp. StorageDescriptor — A StorageDescriptor object. Provides information about the physical location where the partition is stored. Parameters — A map array of key-value pairs.

Managing Partitions for ETL Output in AWS Glue

Each key is a Key string, not less than 1 or more than bytes long, matching the Single-line string pattern. LastAnalyzedTime — Timestamp. The values of the partition.

Although this parameter is not required by the SDK, you must specify this parameter for a valid input. The values for the keys for the new partition must be passed as an array of String objects that must be ordered in the same order as the partition keys appearing in the Amazon S3 prefix. Otherwise AWS Glue will add the values to the wrong keys. Partitions — An array of Partition objects. RootPath — UTF-8 string, not less than 1 or more than bytes long, matching the Single-line string pattern.

Defines a non-overlapping region of a table's partitions, allowing multiple requests to be executed in parallel. SegmentNumber — Required: Number integernot more than None. The zero-based index number of the segment. For example, if the total number of segments is 4, SegmentNumber values range from 0 through 3.

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TotalSegments — Required: Number integernot less than 1 or more than ErrorDetail — An ErrorDetail object. CatalogId — Catalog id string, not less than 1 or more than bytes long, matching the Single-line string pattern. DatabaseName — Required: UTF-8 string, not less than 1 or more than bytes long, matching the Single-line string pattern.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

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Once I execute the Glue crawler to run on the bucket everything works as expected except the types of the partitions keys. The Crawler configures them in the catalog as String type instead of int. I know it can be changed manually later and set the Crawler config to Add new columns only. Glue crawlers always treat partition keys as type string and unfortunately there is no configuration option available to change this behavior.

Learn more. Asked 1 year, 2 months ago. Active 8 months ago. Viewed times. Also using Athena to query this data. The Crawler configures them in the catalog as String type instead of int Is there a configuration to define the default type of the partition keys?

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Alex Stanovsky Alex Stanovsky 5 5 silver badges 19 19 bronze badges. Have you found solution so far? Get stuck with the same problem: all my partition keys are of type int, but crawler discovers them as strings This manual adjustment doesn't fit well into provisioning automation. Also Add new columns only option doesn't work well, when the schema is subjected to change once in a while, as it's so easy to forget this particular crawler config value Active Oldest Votes.

Yuriy Bondaruk Yuriy Bondaruk 2, 1 1 gold badge 12 12 silver badges 31 31 bronze badges. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. The Overflow Blog. Featured on Meta. Feedback on Q2 Community Roadmap.Did you find this page useful? Do you have a suggestion?

Give us feedback or send us a pull request on GitHub. See the User Guide for help getting started. See 'aws help' for descriptions of global parameters. Multiple API calls may be issued in order to retrieve the entire data set of results. You can disable pagination by providing the --no-paginate argument. When using --output text and the --query argument on a paginated response, the --query argument must extract data from the results of the following query expressions: Partitions.

Checks whether the values of the two operands are equal; if yes, then the condition becomes true. Checks whether the values of two operands are equal; if the values are not equal, then the condition becomes true.

Checks whether the value of the left operand is greater than the value of the right operand; if yes, then the condition becomes true. Checks whether the value of the left operand is less than the value of the right operand; if yes, then the condition becomes true. Checks whether the value of the left operand is greater than or equal to the value of the right operand; if yes, then the condition becomes true.

Checks whether the value of the left operand is less than or equal to the value of the right operand; if yes, then the condition becomes true. The following list shows the valid operators on each type.

The JSON string follows the format provided by --generate-cli-skeleton. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. A token to specify where to start paginating.

This is the NextToken from a previously truncated response.

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The size of each page to get in the AWS service call. This does not affect the number of items returned in the command's output. Setting a smaller page size results in more calls to the AWS service, retrieving fewer items in each call. This can help prevent the AWS service calls from timing out.If you've got a moment, please tell us what we did right so we can do more of it.

Thanks for letting us know this page needs work. We're sorry we let you down. If you've got a moment, please tell us how we can make the documentation better. Name — Required: UTF-8 string, not less than 1 or more than bytes long, matching the Single-line string pattern.

DatabaseName — UTF-8 string, not less than 1 or more than bytes long, matching the Single-line string pattern. The name of the database where the table metadata resides.

For Hive compatibility, this must be all lowercase. Description — Description string, not more than bytes long, matching the URI address multi-line string pattern. Owner — UTF-8 string, not less than 1 or more than bytes long, matching the Single-line string pattern.

CreateTime — Timestamp. UpdateTime — Timestamp. LastAccessTime — Timestamp.

aws glue partition keys

The last time that the table was accessed. This is usually taken from HDFS, and might not be reliable. LastAnalyzedTime — Timestamp. Retention — Number integernot more than None. StorageDescriptor — A StorageDescriptor object. A storage descriptor containing information about the physical storage of this table. PartitionKeys — An array of Column objects.If you've got a moment, please tell us what we did right so we can do more of it.

Thanks for letting us know this page needs work. We're sorry we let you down. If you've got a moment, please tell us how we can make the documentation better. Run a crawler that connects to one or more data stores, determines the data structures, and writes tables into the Data Catalog. The crawler uses built-in or custom classifiers to recognize the structure of the data. You can run your crawler on a schedule. For more information, see Defining Crawlers. Migrate an Apache Hive metastore.

When you define a table manually using the console or an API, you specify the table schema and the value of a classification field that indicates the type and format of the data in the data source.

If a crawler creates the table, the data format and schema are determined by either a built-in classifier or a custom classifier. For example, to improve query performance, a partitioned table might separate monthly data into different files using the name of the month as a key.

In AWS Glue, table definitions include the partitioning key of a table. When AWS Glue evaluates the data in Amazon S3 folders to catalog a table, it determines whether an individual table or a partitioned table is added.

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For example, you might own an Amazon S3 bucket named my-app-bucketwhere you store both iOS and Android app sales data. The data is partitioned by year, month, and day.

The data files for iOS and Android sales have the same schema, data format, and compression format. The following Amazon S3 listing of my-app-bucket shows some of the partitions.

Crawlers running on a schedule can add new partitions and update the tables with any schema changes. This also applies to tables migrated from an Apache Hive metastore. To do this, when you define a crawler, instead of specifying one or more data stores as the source of a crawl, you specify one or more existing Data Catalog tables. The crawler then crawls the data stores specified by the catalog tables. In this case, no new tables are created; instead, your manually created tables are updated.

The following are other reasons why you might want to manually create catalog tables and specify catalog tables as the crawler source:.AWS Glue provides enhanced support for working with datasets that are organized into Hive-style partitions. DynamicFrames represent a distributed collection of data without requiring you to specify a schema. You can now push down predicates when creating DynamicFrames to filter out partitions and avoid costly calls to S3.

We have also added support for writing DynamicFrames directly into partitioned directories without converting them to Apache Spark DataFrames. Partitioning has emerged as an important technique for organizing datasets so that they can be queried efficiently by a variety of big data systems.

Data is organized in a hierarchical directory structure based on the distinct values of one or more columns. For example, you might decide to partition your application logs in Amazon S3 by date—broken down by year, month, and day. This can significantly improve the performance of applications that need to read only a few partitions. In this post, we show you how to efficiently process partitioned datasets using AWS Glue.

First, we cover how to set up a crawler to automatically scan your partitioned dataset and create a table and partitions in the AWS Glue Data Catalog. You can now filter partitions using SQL expressions or user-defined functions to avoid listing and reading unnecessary data from Amazon S3. A sample dataset containing one month of activity from January is available at the following location:.

This dataset is partitioned by year, month, and day, so an actual file will be at a path like the following:.

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This template creates a stack that contains the following:. To run this template, you must provide an S3 bucket and prefix where you can write output data in the next section.

The role that this template creates will have permission to write to this bucket only. You also need to provide a public SSH key for connecting to the development endpoint.

In addition to inferring file types and schemas, crawlers automatically identify the partition structure of your dataset and populate the AWS Glue Data Catalog. This ensures that your data is correctly grouped into logical tables and makes the partition columns available for querying in AWS Glue ETL jobs or query engines like Amazon Athena.

The partitions should look like the following:. They are great for debugging and exploratory analysis, and can be used to develop and test scripts before migrating them to a recurring job. If you ran the AWS CloudFormation template in the previous section, then you already have a development endpoint named partition-endpoint in your account.

Partition API

Otherwise, you can follow the instructions in this development endpoint tutorial. In either case, you need to set up an Apache Zeppelin notebook, either locallyor on an EC2 instance.

The following examples are all written in the Scala programming language, but they can all be implemented in Python with minimal changes. This is only necessary when running in a Zeppelin notebook.

Next, read the GitHub data into a DynamicFramewhich is the primary data structure that is used in AWS Glue scripts to represent a distributed collection of data. The following snippet creates a DynamicFrame by referencing the Data Catalog table that you just crawled and then prints the schema:. This paragraph takes about 5 minutes to run on a standard size AWS Glue development endpoint. After it runs, you should see the following output:. Note that the partition columns yearmonthand day were automatically added to each record.

This snippet defines the filterWeekend function that uses the Java Calendar class to identify those records where the partition columns year, month, and day fall on a weekend. This seems reasonable—about 22 percent of the events fell on the weekend, and about 29 percent of the days that month fell on the weekend 9 out of So people are using GitHub slightly less on the weekends, but there is still a lot of activity!

What is AWS Glue?

But as you try to process more data, you will spend an increasing amount of time reading records only to immediately discard them. Then you list and read only the partitions from S3 that you need to process. This predicate can be any SQL expression or user-defined function as long as it uses only the partition columns for filtering. The following snippet shows how to use this functionality to read only those partitions occurring on a weekend:.

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Note that the pushdownPredicate parameter is also available in Python. The corresponding call in Python is as follows:.If you've got a moment, please tell us what we did right so we can do more of it.

Thanks for letting us know this page needs work. We're sorry we let you down. If you've got a moment, please tell us how we can make the documentation better. Partitioning is an important technique for organizing datasets so they can be queried efficiently. It organizes data in a hierarchical directory structure based on the distinct values of one or more columns. For example, you might decide to partition your application logs in Amazon Simple Storage Service Amazon S3 by date, broken down by year, month, and day.

Crawlers not only infer file types and schemas, they also automatically identify the partition structure of your dataset when they populate the AWS Glue Data Catalog. After you crawl a table, you can view the partitions that the crawler created by navigating to the table on the AWS Glue console and choosing View Partitions.

To change the default names on the console, navigate to the table, choose Edit Schemaand modify the names of the partition columns there. In your ETL scripts, you can then filter on the partition columns. In many cases, you can use a pushdown predicate to filter on partitions without having to list and read all the files in your dataset.

Instead of reading the entire dataset and then filtering in a DynamicFrame, you can apply the filter directly on the partition metadata in the Data Catalog. Then you only list and read what you actually need into a DynamicFrame. This creates a DynamicFrame that loads only the partitions in the Data Catalog that satisfy the predicate expression. Depending on how small a subset of your data you are loading, this can save a great deal of processing time.

In addition to Hive-style partitioning for Amazon S3 paths, Apache Parquet and Apache ORC file formats further partition each file into blocks of data that represent column values.

aws glue partition keys

AWS Glue supports pushdown predicates for both Hive-style partitions and block partitions in these formats. In this way, you can prune unnecessary Amazon S3 partitions in Parquet and ORC formats, and skip blocks that you determine are unnecessary using column statistics. By default, a DynamicFrame is not partitioned when it is written. All of the output files are written at the top level of the specified output path. However, DynamicFrames now support native partitioning using a sequence of keys, using the partitionKeys option when you create a sink.

For example, the following Python code writes out a dataset to Amazon S3 in the Parquet format, into directories partitioned by the type field. From there, you can process these partitions using other systems, such as Amazon Athena. Javascript is disabled or is unavailable in your browser. Please refer to your browser's Help pages for instructions.

Pushdown Predicates Writing Partitions. Did this page help you? Thanks for letting us know we're doing a good job! Document Conventions. Format Options.

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