# DynamicFrameReader class
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##  — methods —
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+ [\$1\$1init\$1\$1](#aws-glue-api-crawler-pyspark-extensions-dynamic-frame-reader-__init__)
+ [from\$1rdd](#aws-glue-api-crawler-pyspark-extensions-dynamic-frame-reader-from_rdd)
+ [from\$1options](#aws-glue-api-crawler-pyspark-extensions-dynamic-frame-reader-from_options)
+ [from\$1catalog](#aws-glue-api-crawler-pyspark-extensions-dynamic-frame-reader-from_catalog)

## \$1\$1init\$1\$1
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**`__init__(glue_context)`**
+ `glue_context` – The [GlueContext class](aws-glue-api-crawler-pyspark-extensions-glue-context.md) to use.

## from\$1rdd
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**`from_rdd(data, name, schema=None, sampleRatio=None)`**

Reads a `DynamicFrame` from a Resilient Distributed Dataset (RDD).
+ `data` – The dataset to read from.
+ `name` – The name to read from.
+ `schema` – The schema to read (optional).
+ `sampleRatio` – The sample ratio (optional).

## from\$1options
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**`from_options(connection_type, connection_options={}, format=None, format_options={}, transformation_ctx="")`**

Reads a `DynamicFrame` using the specified connection and format.
+ `connection_type` – The connection type. Valid values include `s3`, `mysql`, `postgresql`, `redshift`, `sqlserver`, `oracle`, `dynamodb`, and `snowflake`.
+ `connection_options` – Connection options, such as path and database table (optional). For more information, see [ Connection types and options for ETL in AWS Glue for Spark ](https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-etl-connect.html). For a `connection_type` of `s3`, Amazon S3 paths are defined in an array.

  ```
  connection_options = {"paths": [ "s3://amzn-s3-demo-bucket/object_a", "s3://amzn-s3-demo-bucket/object_b"]}
  ```

  For JDBC connections, several properties must be defined. Note that the database name must be part of the URL. It can optionally be included in the connection options.
**Warning**  
Storing passwords in your script is not recommended. Consider using `boto3` to retrieve them from AWS Secrets Manager or the AWS Glue Data Catalog.

  ```
  connection_options = {"url": "jdbc-url/database", "user": "username", "password": passwordVariable,"dbtable": "table-name", "redshiftTmpDir": "s3-tempdir-path"} 
  ```

  For a JDBC connection that performs parallel reads, you can set the hashfield option. For example:

  ```
  connection_options = {"url": "jdbc-url/database", "user": "username", "password": passwordVariable,"dbtable": "table-name", "redshiftTmpDir": "s3-tempdir-path" , "hashfield": "month"} 
  ```

  For more information, see [Reading from JDBC tables in parallel](run-jdbc-parallel-read-job.md). 
+ `format` – A format specification (optional). This is used for an Amazon Simple Storage Service (Amazon S3) or an AWS Glue connection that supports multiple formats. See [Data format options for inputs and outputs in AWS Glue for Spark](aws-glue-programming-etl-format.md) for the formats that are supported.
+ `format_options` – Format options for the specified format. See [Data format options for inputs and outputs in AWS Glue for Spark](aws-glue-programming-etl-format.md) for the formats that are supported.
+ `transformation_ctx` – The transformation context to use (optional).
+ `push_down_predicate` – Filters partitions without having to list and read all the files in your dataset. For more information, see [Pre-Filtering Using Pushdown Predicates](https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-etl-partitions.html#aws-glue-programming-etl-partitions-pushdowns).

## from\$1catalog
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**`from_catalog(database, table_name, redshift_tmp_dir="", transformation_ctx="", push_down_predicate="", additional_options={})`**

Reads a `DynamicFrame` using the specified catalog namespace and table name.
+ `database` – The database to read from.
+ `table_name` – The name of the table to read from.
+ `redshift_tmp_dir` – An Amazon Redshift temporary directory to use (optional if not reading data from Redshift).
+ `transformation_ctx` – The transformation context to use (optional).
+ `push_down_predicate` – Filters partitions without having to list and read all the files in your dataset. For more information, see [Pre-filtering using pushdown predicates](aws-glue-programming-etl-partitions.md#aws-glue-programming-etl-partitions-pushdowns).
+ `additional_options` – Additional options provided to AWS Glue. 
  + To use a JDBC connection that performs parallel reads, you can set the `hashfield`, `hashexpression`, or `hashpartitions` options. For example:

    ```
    additional_options = {"hashfield": "month"} 
    ```

    For more information, see [Reading from JDBC tables in parallel](run-jdbc-parallel-read-job.md). 
  + To pass a catalog expression to filter based on the index columns, you can see the `catalogPartitionPredicate` option.

    `catalogPartitionPredicate` — You can pass a catalog expression to filter based on the index columns. This pushes down the filtering to the server side. For more information, see [AWS Glue Partition Indexes](https://docs.aws.amazon.com/glue/latest/dg/partition-indexes.html). Note that `push_down_predicate` and `catalogPartitionPredicate` use different syntaxes. The former one uses Spark SQL standard syntax and the later one uses JSQL parser.

    For more information, see [Managing partitions for ETL output in AWS Glue](aws-glue-programming-etl-partitions.md).