Configuring a crawler - AWS Glue

Configuring a crawler

A crawler accesses your data store, extracts metadata, and creates table definitions in the AWS Glue Data Catalog. The Crawlers pane in the AWS Glue console lists all the crawlers that you create. The list displays status and metrics from the last run of your crawler.


If you choose to bring in your own JDBC driver versions, AWS Glue crawlers will consume resources in AWS Glue jobs and Amazon S3 buckets to ensure your provided driver are run in your environment. The additional usage of resources will be reflected in your account. Additionally, providing your own JDBC driver does not mean that the crawler is able to leverage all of the driver's features. Drivers are limited to the properties described in Adding an AWS Glue connection.

To configure a crawler
  1. Sign in to the AWS Management Console and open the AWS Glue console at Choose Crawlers in the navigation pane.

  2. Choose Create crawler, and follow the instructions in the Add crawler wizard. The wizard will guide you the steps required to create a crawler. If you want to add custom calssifiers to define the schema, see Adding classifiers to a crawler in AWS Glue.

Step 1: Set crawler properties

Enter a name for your crawler and description (optional). Optionally, you can tag your crawler with a Tag key and optional Tag value. Once created, tag keys are read-only. Use tags on some resources to help you organize and identify them. For more information, see AWS tags in AWS Glue.


Name may contain letters (A-Z), numbers (0-9), hyphens (-), or underscores (_), and can be up to 255 characters long.


Descriptions can be up to 2048 characters long.


Use tags to organize and identify your resources. For more information, see the following:

Step 2: Choose data sources and classifiers

Data source configuration

Select the appropriate option for Is your data already mapped to AWS Glue tables? choose 'Not yet' or 'Yes'. By default, 'Not yet' is selected.

The crawler can access data stores directly as the source of the crawl, or it can use existing tables in the Data Catalog as the source. If the crawler uses existing catalog tables, it crawls the data stores that are specified by those catalog tables.

  • Not yet: Select one or more data sources to be crawled. A crawler can crawl multiple data stores of different types (Amazon S3, JDBC, and so on).

    You can configure only one data store at a time. After you have provided the connection information and include paths and exclude patterns, you then have the option of adding another data store.

  • Yes: Select existing tables from your AWS Glue Data Catalog. The catalog tables specify the data stores to crawl. The crawler can crawl only catalog tables in a single run; it can't mix in other source types.

    A common reason to specify a catalog table as the source is when you create the table manually (because you already know the structure of the data store) and you want a crawler to keep the table updated, including adding new partitions. For a discussion of other reasons, see Updating manually created Data Catalog tables using crawlers.

    When you specify existing tables as the crawler source type, the following conditions apply:

    • Database name is optional.

    • Only catalog tables that specify Amazon S3 or Amazon DynamoDB data stores are permitted.

    • No new catalog tables are created when the crawler runs. Existing tables are updated as needed, including adding new partitions.

    • Deleted objects found in the data stores are ignored; no catalog tables are deleted. Instead, the crawler writes a log message. (SchemaChangePolicy.DeleteBehavior=LOG)

    • The crawler configuration option to create a single schema for each Amazon S3 path is enabled by default and cannot be disabled. (TableGroupingPolicy=CombineCompatibleSchemas) For more information, see How to create a single schema for each Amazon S3 include path.

    • You can't mix catalog tables as a source with any other source types (for example Amazon S3 or Amazon DynamoDB).

Data sources

Select or add the list of data sources to be scanned by the crawler.

(Optional) If you choose JDBC as the data source, you can use your own JDBC drivers when specifying the Connection access where the driver info is stored.

Include path

When evaluating what to include or exclude in a crawl, a crawler starts by evaluating the required include path. For Amazon S3, MongoDB, MongoDB Atlas, Amazon DocumentDB (with MongoDB compatibility), and relational data stores, you must specify an include path.

For an Amazon S3 data store

Choose whether to specify a path in this account or in a different account, and then browse to choose an Amazon S3 path.

For Amazon S3 data stores, include path syntax is bucket-name/folder-name/file-name.ext. To crawl all objects in a bucket, you specify just the bucket name in the include path. The exclude pattern is relative to the include path

For a Delta Lake data store

Specify one or more Amazon S3 paths to Delta tables as s3://bucket/prefix/object.

For an Iceberg or Hudi data store

Specify one or more Amazon S3 paths that contain folders with Iceberg or Hudi table metadata as s3://bucket/prefix.

For a Hudi data store, the Hudi folder may be located in a child folder of the root folder. The crawler will scan all folders underneath a path for a Hudi folder.

For a JDBC data store

Enter <database>/<schema>/<table> or <database>/<table>, depending on the database product. Oracle Database and MySQL don’t support schema in the path. You can substitute the percent (%) character for <schema> or <table>. For example, for an Oracle database with a system identifier (SID) of orcl, enter orcl/% to import all tables to which the user named in the connection has access.


This field is case-sensitive.

For a MongoDB, MongoDB Atlas, or Amazon DocumentDB data store

Enter database/collection.

For MongoDB, MongoDB Atlas, and Amazon DocumentDB (with MongoDB compatibility), the syntax is database/collection.

For JDBC data stores, the syntax is either database-name/schema-name/table-name or database-name/table-name. The syntax depends on whether the database engine supports schemas within a database. For example, for database engines such as MySQL or Oracle, don't specify a schema-name in your include path. You can substitute the percent sign (%) for a schema or table in the include path to represent all schemas or all tables in a database. You cannot substitute the percent sign (%) for database in the include path.

Maximum transversal depth (for Iceberg or Hudi data stores only)

Defines the maximum depth of the Amazon S3 path that the crawler can traverse to discover the Iceberg or Hudi metadata folder in your Amazon S3 path. The purpose of this parameter is to limit the crawler run time. The default value is 10 and the maximum is 20.

Exclude patterns

These enable you to exclude certain files or tables from the crawl. The exclude path is relative to the include path. For example, to exclude a table in your JDBC data store, type the table name in the exclude path.

A crawler connects to a JDBC data store using an AWS Glue connection that contains a JDBC URI connection string. The crawler only has access to objects in the database engine using the JDBC user name and password in the AWS Glue connection. The crawler can only create tables that it can access through the JDBC connection. After the crawler accesses the database engine with the JDBC URI, the include path is used to determine which tables in the database engine are created in the Data Catalog. For example, with MySQL, if you specify an include path of MyDatabase/%, then all tables within MyDatabase are created in the Data Catalog. When accessing Amazon Redshift, if you specify an include path of MyDatabase/%, then all tables within all schemas for database MyDatabase are created in the Data Catalog. If you specify an include path of MyDatabase/MySchema/%, then all tables in database MyDatabase and schema MySchema are created.

After you specify an include path, you can then exclude objects from the crawl that your include path would otherwise include by specifying one or more Unix-style glob exclude patterns. These patterns are applied to your include path to determine which objects are excluded. These patterns are also stored as a property of tables created by the crawler. AWS Glue PySpark extensions, such as create_dynamic_frame.from_catalog, read the table properties and exclude objects defined by the exclude pattern.

AWS Glue supports the following kinds of glob patterns in the exclude pattern.

Exclude pattern Description
*.csv Matches an Amazon S3 path that represents an object name in the current folder ending in .csv
*.* Matches all object names that contain a dot
*.{csv,avro} Matches object names ending with .csv or .avro
foo.? Matches object names starting with foo. that are followed by a single character extension
myfolder/* Matches objects in one level of subfolder from myfolder, such as /myfolder/mysource
myfolder/*/* Matches objects in two levels of subfolders from myfolder, such as /myfolder/mysource/data
myfolder/** Matches objects in all subfolders of myfolder, such as /myfolder/mysource/mydata and /myfolder/mysource/data
myfolder** Matches subfolder myfolder as well as files below myfolder, such as /myfolder and /myfolder/mydata.txt
Market* Matches tables in a JDBC database with names that begin with Market, such as Market_us and Market_fr

AWS Glue interprets glob exclude patterns as follows:

  • The slash (/) character is the delimiter to separate Amazon S3 keys into a folder hierarchy.

  • The asterisk (*) character matches zero or more characters of a name component without crossing folder boundaries.

  • A double asterisk (**) matches zero or more characters crossing folder or schema boundaries.

  • The question mark (?) character matches exactly one character of a name component.

  • The backslash (\) character is used to escape characters that otherwise can be interpreted as special characters. The expression \\ matches a single backslash, and \{ matches a left brace.

  • Brackets [ ] create a bracket expression that matches a single character of a name component out of a set of characters. For example, [abc] matches a, b, or c. The hyphen (-) can be used to specify a range, so [a-z] specifies a range that matches from a through z (inclusive). These forms can be mixed, so [abce-g] matches a, b, c, e, f, or g. If the character after the bracket ([) is an exclamation point (!), the bracket expression is negated. For example, [!a-c] matches any character except a, b, or c.

    Within a bracket expression, the *, ?, and \ characters match themselves. The hyphen (-) character matches itself if it is the first character within the brackets, or if it's the first character after the ! when you are negating.

  • Braces ({ }) enclose a group of subpatterns, where the group matches if any subpattern in the group matches. A comma (,) character is used to separate the subpatterns. Groups cannot be nested.

  • Leading period or dot characters in file names are treated as normal characters in match operations. For example, the * exclude pattern matches the file name .hidden.

Example Amazon S3 exclude patterns

Each exclude pattern is evaluated against the include path. For example, suppose that you have the following Amazon S3 directory structure:

/mybucket/myfolder/ departments/ finance.json market-us.json market-emea.json market-ap.json employees/ hr.json john.csv jane.csv juan.txt

Given the include path s3://mybucket/myfolder/, the following are some sample results for exclude patterns:

Exclude pattern Results
departments/** Excludes all files and folders below departments and includes the employees folder and its files
departments/market* Excludes market-us.json, market-emea.json, and market-ap.json
**.csv Excludes all objects below myfolder that have a name ending with .csv
employees/*.csv Excludes all .csv files in the employees folder
Example Excluding a subset of Amazon S3 partitions

Suppose that your data is partitioned by day, so that each day in a year is in a separate Amazon S3 partition. For January 2015, there are 31 partitions. Now, to crawl data for only the first week of January, you must exclude all partitions except days 1 through 7:

2015/01/{[!0],0[8-9]}**, 2015/0[2-9]/**, 2015/1[0-2]/**

Take a look at the parts of this glob pattern. The first part, 2015/01/{[!0],0[8-9]}**, excludes all days that don't begin with a "0" in addition to day 08 and day 09 from month 01 in year 2015. Notice that "**" is used as the suffix to the day number pattern and crosses folder boundaries to lower-level folders. If "*" is used, lower folder levels are not excluded.

The second part, 2015/0[2-9]/**, excludes days in months 02 to 09, in year 2015.

The third part, 2015/1[0-2]/**, excludes days in months 10, 11, and 12, in year 2015.

Example JDBC exclude patterns

Suppose that you are crawling a JDBC database with the following schema structure:

MyDatabase/MySchema/ HR_us HR_fr Employees_Table Finance Market_US_Table Market_EMEA_Table Market_AP_Table

Given the include path MyDatabase/MySchema/%, the following are some sample results for exclude patterns:

Exclude pattern Results
HR* Excludes the tables with names that begin with HR
Market_* Excludes the tables with names that begin with Market_
**_Table Excludes all tables with names that end with _Table
Additional crawler source parameters

Each source type requires a different set of additional parameters. The following is an incomplete list:


Select or add an AWS Glue connection. For information about connections, see Connecting to data.

Additional metadata - optional (for JDBC data stores)

Select additional metadata properties for the crawler to crawl.

  • Comments: Crawl associated table level and column level comments.

  • Raw types: Persist the raw datatypes of the table columns in additional metadata. As a default behavior, the crawler translates the raw datatypes to Hive-compatible types.

JDBC Driver Class name - optional (for JDBC data stores)

Type a custom JDBC driver class name for the crawler to connect to the data source:

  • Postgres: org.postgresql.Driver

  • MySQL: com.mysql.jdbc.Driver, com.mysql.cj.jdbc.Driver

  • Redshift:,

  • Oracle: oracle.jdbc.driver.OracleDriver

  • SQL Server:

JDBC Driver S3 Path - optional (for JDBC data stores)

Choose an existing Amazon S3 path to a .jar file. This is where the .jar file will be stored when using a custom JDBC driver for the crawler to connect to the data source.

Enable data sampling (for Amazon DynamoDB, MongoDB, MongoDB Atlas, and Amazon DocumentDB data stores only)

Select whether to crawl a data sample only. If not selected the entire table is crawled. Scanning all the records can take a long time when the table is not a high throughput table.

Create tables for querying (for Delta Lake data stores only)

Select how you want to create the Delta Lake tables:

  • Create Native tables: Allow integration with query engines that support querying of the Delta transaction log directly.

  • Create Symlink tables: Create a symlink manifest folder with manifest files partitioned by the partition keys, based on the specified configuration parameters.

Scanning rate - optional (for DynamoDB data stores only)

Specify the percentage of the DynamoDB table Read Capacity Units to use by the crawler. Read capacity units is a term defined by DynamoDB, and is a numeric value that acts as rate limiter for the number of reads that can be performed on that table per second. Enter a value between 0.1 and 1.5. If not specified, defaults to 0.5% for provisioned tables and 1/4 of maximum configured capacity for on-demand tables. Note that only provisioned capacity mode should be used with AWS Glue crawlers.


For DynamoDB data stores, set the provisioned capacity mode for processing reads and writes on your tables. The AWS Glue crawler should not be used with the on-demand capacity mode.

Network connection - optional (for Amazon S3 data stores only)

Optionally include a Network connection to use with this Amazon S3 target. Note that each crawler is limited to one Network connection so any other Amazon S3 targets will also use the same connection (or none, if left blank).

For information about connections, see Connecting to data.

Sample only a subset of files and Sample size (for Amazon S3 data stores only)

Specify the number of files in each leaf folder to be crawled when crawling sample files in a dataset. When this feature is turned on, instead of crawling all the files in this dataset, the crawler randomly selects some files in each leaf folder to crawl.

The sampling crawler is best suited for customers who have previous knowledge about their data formats and know that schemas in their folders do not change. Turning on this feature will significantly reduce crawler runtime.

A valid value is an integer between 1 and 249. If not specified, all the files are crawled.

Subsequent crawler runs

This field is a global field that affects all Amazon S3 data sources.

  • Crawl all sub-folders: Crawl all folders again with every subsequent crawl.

  • Crawl new sub-folders only: Only Amazon S3 folders that were added since the last crawl will be crawled. If the schemas are compatible, new partitions will be added to existing tables. For more information, see Incremental crawls for adding new partitions.

  • Crawl based on events: Rely on Amazon S3 events to control what folders to crawl. For more information, see Accelerating crawls using Amazon S3 event notifications.

Custom classifiers - optional

Define custom classifiers before defining crawlers. A classifier checks whether a given file is in a format the crawler can handle. If it is, the classifier creates a schema in the form of a StructType object that matches that data format.

For more information, see Adding classifiers to a crawler in AWS Glue.

Step 3: Configure security settings

IAM role

The crawler assumes this role. It must have permissions similar to the AWS managed policy AWSGlueServiceRole. For Amazon S3 and DynamoDB sources, it must also have permissions to access the data store. If the crawler reads Amazon S3 data encrypted with AWS Key Management Service (AWS KMS), then the role must have decrypt permissions on the AWS KMS key.

For an Amazon S3 data store, additional permissions attached to the role would be similar to the following:

{ "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "s3:GetObject", "s3:PutObject" ], "Resource": [ "arn:aws:s3:::bucket/object*" ] } ] }

For an Amazon DynamoDB data store, additional permissions attached to the role would be similar to the following:

{ "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "dynamodb:DescribeTable", "dynamodb:Scan" ], "Resource": [ "arn:aws:dynamodb:region:account-id:table/table-name*" ] } ] }

In order to add your own JDBC driver, additional permissions need to be added.

  • Grant permissions for the following job actions: CreateJob, DeleteJob, GetJob, GetJobRun, StartJobRun.

  • Grant permissions for Amazon S3 actions: s3:DeleteObjects, s3:GetObject, s3:ListBucket, s3:PutObject.


    The s3:ListBucket is not needed if the Amazon S3 bucket policy is disabled.

  • Grant service principal access to bucket/folder in the Amazon S3 policy.

Example Amazon S3 policy:

{ "Version": "2012-10-17", "Statement": [ { "Sid": "VisualEditor0", "Effect": "Allow", "Action": [ "s3:PutObject", "s3:GetObject", "s3:ListBucket", "s3:DeleteObject" ], "Resource": [ "arn:aws:s3:::bucket-name/driver-parent-folder/driver.jar", "arn:aws:s3:::bucket-name" ] } ] }

AWS Glue creates the following folders (_crawler and _glue_job_crawler at the same level as the JDBC driver in your Amazon S3 bucket. For example, if the driver path is <s3-path/driver_folder/driver.jar>, then the following folders will be created if they do not already exist:

  • <s3-path/driver_folder/_crawler>

  • <s3-path/driver_folder/_glue_job_crawler>

Optionally, you can add a security configuration to a crawler to specify at-rest encryption options.

For more information, see Step 2: Create an IAM role for AWS Glue and Identity and access management for AWS Glue.

Lake Formation configuration - optional

Allow the crawler to use Lake Formation credentials for crawling the data source.

Checking Use Lake Formation credentials for crawling S3 data source will allow the crawler to use Lake Formation credentials for crawling the data source. If the data source belongs to another account, you must provide the registered account ID. Otherwise, the crawler will crawl only those data sources associated to the account. Only applicable to Amazon S3 and Data Catalog data sources.

Security configuration - optional

Settings include security configurations. For more information, see the following:


Once a security configuration has been set on a crawler, you can change, but you cannot remove it. To lower the level of security on a crawler, explicitly set the security feature to DISABLED within your configuration, or create a new crawler.

Step 4: Set output and scheduling

Output configuration

Options include how the crawler should handle detected schema changes, deleted objects in the data store, and more. For more information, see Customizing crawler behavior

Crawler schedule

You can run a crawler on demand or define a time-based schedule for your crawlers and jobs in AWS Glue. The definition of these schedules uses the Unix-like cron syntax. For more information, see Scheduling an AWS Glue crawler.

Step 5: Review and create

Review the crawler settings you configured, and create the crawler.