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Convert Oracle XML BLOB knowledge to JSON utilizing Amazon EMR and cargo to Amazon Redshift

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In legacy relational database administration programs, knowledge is saved in a number of advanced knowledge varieties, such XML, JSON, BLOB, or CLOB. This knowledge may comprise helpful info that’s usually troublesome to remodel into insights, so that you is perhaps in search of methods to load and use this knowledge in a contemporary cloud knowledge warehouse similar to Amazon Redshift. One such instance is migrating knowledge from a legacy Oracle database with XML BLOB fields to Amazon Redshift, by performing preprocessing and conversion of XML to JSON utilizing Amazon EMR. On this submit, we describe an answer structure for this use case, and present you tips on how to implement the code to deal with the XML conversion.

Answer overview

Step one in any knowledge migration challenge is to seize and ingest the info from the supply database. For this process, we use AWS Database Migration Service (AWS DMS), a service that helps you migrate databases to AWS shortly and securely. On this instance, we use AWS DMS to extract knowledge from an Oracle database with XML BLOB fields and stage the identical knowledge in Amazon Easy Storage Service (Amazon S3) in Apache Parquet format. Amazon S3 is an object storage service providing industry-leading scalability, knowledge availability, safety, and efficiency, and is the storage of selection for establishing knowledge lakes on AWS.

After the info is ingested into an S3 staging bucket, we used Amazon EMR to run a Spark job to carry out the conversion of XML fields to JSON fields, and the outcomes are loaded in a curated S3 bucket. Amazon EMR runtime for Apache Spark might be over 3 times sooner than clusters with out EMR runtime, and has 100% API compatibility with normal Apache Spark. This improved efficiency means your workloads run sooner and it saves you compute prices, with out making any adjustments to your software.

Lastly, reworked and curated knowledge is loaded into Amazon Redshift tables utilizing the COPY command. The Amazon Redshift desk construction ought to match the variety of columns and the column knowledge varieties within the supply file. As a result of we saved the info as a Parquet file, we specify the SERIALIZETOJSON choice within the COPY command. This enables us to load advanced varieties, similar to construction and array, in a column outlined as SUPER knowledge kind within the desk.

The next structure diagram exhibits the end-to-end workflow.

Intimately, AWS DMS migrates knowledge from the supply database tables into Amazon S3, in Parquet format. Apache Spark on Amazon EMR reads the uncooked knowledge, transforms the XML knowledge kind into JSON, and saves the info to the curated S3 bucket. In our code, we used an open-source library, referred to as spark-xml, to parse and question the XML knowledge.

In the remainder of this submit, we assume that the AWS DMS duties have already run and created the supply Parquet recordsdata within the S3 staging bucket. If you wish to arrange AWS DMS to learn from an Oracle database with LOB fields, discuss with Successfully migrating LOB knowledge to Amazon S3 from Amazon RDS for Oracle with AWS DMS or watch the video Migrate Oracle to S3 Information lake through AWS DMS.

Conditions

If you wish to comply with together with the examples on this submit utilizing your AWS account, we offer an AWS CloudFormation template you may launch by selecting Launch Stack:

BDB-2063-launch-cloudformation-stack

Present a stack identify and depart the default settings for every part else. Await the stack to show Create Full (this could solely take a couple of minutes) earlier than transferring on to the opposite sections.

The template creates the next sources:

  • A digital non-public cloud (VPC) with two non-public subnets which have routes to an Amazon S3 VPC endpoint
  • The S3 bucket {stackname}-s3bucket-{xxx}, which incorporates the next folders:
    • libs – Comprises the JAR file so as to add to the pocket book
    • notebooks – Comprises the pocket book to interactively check the code
    • knowledge – Comprises the pattern knowledge
  • An Amazon Redshift cluster, in one of many two non-public subnets, with a database named rs_xml_db and a schema named rs_xml
  • A secret (rs_xml_db) in AWS Secrets and techniques Supervisor
  • An EMR cluster

The CloudFormation template shared on this submit is only for demonstration functions solely. Please conduct your personal safety assessment and incorporate greatest practices previous to any manufacturing deployment utilizing artifacts from the submit.

Lastly, some primary data of Python and Spark DataFrames may also help you assessment the transformation code, however isn’t obligatory to finish the instance.

Understanding the pattern knowledge

On this submit, we use school college students’ course and topics pattern knowledge that we created. Within the supply system, knowledge consists of flat construction fields, like course_id and course_name, and an XML area that features all of the course materials and topics concerned within the respective course. The next screenshot is an instance of the supply knowledge, which is staged in an S3 bucket as a prerequisite step.

We are able to observe that the column study_material_info is an XML kind area and incorporates nested XML tags in it. Let’s see tips on how to convert this nested XML area to JSON within the subsequent steps.

Run a Spark job in Amazon EMR to remodel the XML fields within the uncooked knowledge to JSON

On this step, we use an Amazon EMR pocket book, which is a managed setting to create and open Jupyter Pocket book and JupyterLab interfaces. It allows you to interactively analyze and visualize knowledge, collaborate with friends, and construct purposes utilizing Apache Spark on EMR clusters. To open the pocket book, comply with these steps:

  1. On the Amazon S3 console, navigate to the bucket you created as a prerequisite step.
  2. Obtain the file within the notebooks folder.
  3. On the Amazon EMR console, select Notebooks within the navigation pane.
  4. Select Create pocket book.
  5. For Pocket book identify, enter a reputation.
  6. For Cluster, choose Select an current cluster.
  7. Choose the cluster you created as a prerequisite.
  8. For Safety Teams, select BDB1909-EMR-LIVY-SG and BDB1909-EMR-Pocket book-SG
  9. For AWS Service Function, select the function bdb1909-emrNotebookRole-{xxx}.
  10. For Pocket book location, specify the S3 path within the notebooks folder (s3://{stackname}-s3bucket-xxx}/notebooks/).
  11. Select Create pocket book.
  12. When the pocket book is created, select Open in JupyterLab.
  13. Add the file you downloaded earlier.
  14. Open the brand new pocket book.

    The pocket book ought to look as proven within the following screenshot, and it incorporates a script written in Scala.
  15. Run the primary two cells to configure Apache Spark with the open-source spark-xml library and import the wanted modules.The spark-xml bundle permits studying XML recordsdata in native or distributed file programs as Spark DataFrames. Though primarily used to transform (parts of) giant XML paperwork right into a DataFrame, spark-xml also can parse XML in a string-valued column in an current DataFrame with the from_xml perform, so as to add it as a brand new column with parsed outcomes as a struct.
  16. To take action, within the third cell, we load the info from the Parquet file generated by AWS DMS right into a DataFrame, then we extract the attribute that incorporates the XML code (STUDY_MATERIAL_INFO) and map it to a string variable identify payloadSchema.
  17. We are able to now use the payloadSchema within the from_xml perform to transform the sphere STUDY_MATERIAL_INFO right into a struct knowledge kind and added it as a column named course_material in a brand new DataFrame parsed.
  18. Lastly, we will drop the unique area and write the parsed DataFrame to our curated zone in Amazon S3.

As a result of construction variations between DataFrame and XML, there are some conversion guidelines from XML knowledge to DataFrame and from DataFrame to XML knowledge. Extra particulars and documentation can be found XML Information Supply for Apache Spark.

After we convert from XML to DataFrame, attributes are transformed as fields with the heading prefix attributePrefix (underscore (_) is the default). For instance, see the next code:

  <ebook class="undergraduate">
    <title lang="en">Introduction to Biology</title>
    <creator>Demo Writer 1</creator>
    <yr>2005</yr>
    <value>30.00</value>
  </ebook>

It produces the next schema:

root
 |-- class: string (nullable = true)
 |-- title: struct (nullable = true)
 |    |-- _VALUE: string (nullable = true)
 |    |-- _lang: string (nullable = true)
 |-- creator: string (nullable = true)
 |-- yr: string (nullable = true)
 |-- value: string (nullable = true)

Subsequent, we’ve got a price in a component that has no youngster components however attributes. The worth is put in a separate area, valueTag. See the next code:

<title lang="en">Introduction to Biology</title>

It produces the next schema, and the tag lang is transformed into the _lang area contained in the DataFrame:

|-- title: struct (nullable = true)
 |    |-- _VALUE: string (nullable = true)
 |    |-- _lang: string (nullable = true)

Copy curated knowledge into Amazon Redshift and question tables seamlessly

As a result of our semi-structured nested dataset is already written within the S3 bucket as Apache Parquet formatted recordsdata, we will use the COPY command with the SERIALIZETOJSON choice to ingest knowledge into Amazon Redshift. The Amazon Redshift desk construction ought to match the metadata of the Parquet recordsdata. Amazon Redshift can exchange any Parquet columns, together with construction and array varieties, with SUPER knowledge columns.

The next code demonstrates CREATE TABLE instance to create a staging desk.

create desk rs_xml_db.public.stg_edw_course_catalog 
(
course_id bigint,
course_name character various(5000),
course_material tremendous
);

The next code makes use of the COPY instance to load from Parquet format:

COPY rs_xml_db.public.stg_edw_course_catalog FROM 's3://<<your Amazon S3 Bucket for curated knowledge>>/knowledge/goal/<<your output parquet file>>' 
IAM_ROLE '<<your IAM function>>' 
FORMAT PARQUET SERIALIZETOJSON;

By utilizing semistructured knowledge assist in Amazon Redshift, you may ingest and retailer semistructured knowledge in your Amazon Redshift knowledge warehouses. With the SUPER knowledge kind and PartiQL language, Amazon Redshift expands the info warehouse functionality to combine with each SQL and NoSQL knowledge sources. The SUPER knowledge kind solely helps as much as 1 MB of knowledge for a person SUPER area or object. Word, the JSON object could also be saved in a SUPER knowledge kind, however studying this knowledge utilizing JSON features at present has a VARCHAR (65535 byte) restrict. See Limitations for extra particulars.

The next instance exhibits how nested JSON might be simply accessed utilizing SELECT statements:

SELECT DISTINCT bk._category
	,bk.creator
	,bk.value
	,bk.yr
	,bk.title._lang
FROM rs_xml_db.public.stg_edw_course_catalog predominant
INNER JOIN predominant.course_material.ebook bk ON true;

The next screenshot exhibits our outcomes.

Clear up

To keep away from incurring future fees, first delete the pocket book and the associated recordsdata on Amazon S3 bucket as defined in this EMR documentation web page then the CloudFormation stack.

Conclusion

This submit demonstrated tips on how to use AWS companies like AWS DMS, Amazon S3, Amazon EMR, and Amazon Redshift to seamlessly work with advanced knowledge varieties like XML and carry out historic migrations when constructing a cloud knowledge lake home on AWS. We encourage you to do this resolution and benefit from all the advantages of those purpose-built companies.

When you have questions or strategies, please depart a remark.

Concerning the authors

Abhilash Nagilla is a Sr. Specialist Options Architect at AWS, serving to public sector clients on their cloud journey with a give attention to AWS analytics companies. Exterior of labor, Abhilash enjoys studying new applied sciences, watching films, and visiting new locations.

Avinash Makey is a Specialist Options Architect at AWS. He helps clients with knowledge and analytics options in AWS. Exterior of labor he performs cricket, tennis and volleyball in free time.

Fabrizio Napolitano is a Senior Specialist SA for DB and Analytics. He has labored within the analytics house for the final 20 years, and has not too long ago and fairly abruptly change into a Hockey Dad after transferring to Canada.

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