Apache Hudi on AWS
1. Introduction
Apache Hudi is an open-source data management framework tailored for managing large datasets on distributed storage systems. It provides capabilities for data ingestion, storage, and querying, enabling efficient ETL operations.
2. Key Concepts
2.1 What is Apache Hudi?
Apache Hudi stands for Hadoop Upserts Deletes and Incrementals. It allows users to perform CRUD operations on large datasets stored in cloud-based or on-premises storage systems.
2.2 Core Features
- Support for both batch and incremental processing.
- Columnar storage format (Parquet) for efficient querying.
- Time travel capabilities for data versioning.
3. Setup on AWS
To set up Apache Hudi on AWS, follow these steps:
- Set up an AWS S3 bucket to store Hudi datasets.
- Create an EMR cluster with Apache Hudi pre-installed.
- Configure access permissions to your S3 bucket.
4. Code Example
Here’s a simple example of how to write data into a Hudi table:
import org.apache.hudi.DataSourceWriteOptions;
import org.apache.hudi.QuickstartUtils;
Dataset data = QuickstartUtils.createData(1000);
data.write()
.format("hudi")
.option(DataSourceWriteOptions.RECORDKEY_FIELD().key(), "uuid")
.option(DataSourceWriteOptions.PRECOMBINE_FIELD().key(), "ts")
.option(DataSourceWriteOptions.TABLE_TYPE().key(), "MERGE_ON_READ")
.option(DataSourceWriteOptions.HIVE_SYNC_ENABLED().key(), "true")
.option(DataSourceWriteOptions.HIVE_DATABASE().key(), "default")
.option(DataSourceWriteOptions.HIVE_TABLE().key(), "hudi_table")
.mode(SaveMode.Overwrite)
.save("s3://your-bucket/hudi_table/");
5. Best Practices
Always monitor and optimize your Hudi tables for performance improvements.
- Use the correct storage type (Copy-on-Write vs. Merge-on-Read) based on your use case.
- Regularly compact your Hudi tables to improve read performance.
- Utilize Hudi's indexing capabilities to optimize query performance.
6. FAQ
What is the difference between Copy-on-Write and Merge-on-Read?
Copy-on-Write stores a new version of the data on each write, while Merge-on-Read keeps base files immutable and applies updates at read time, which can be more performant for certain workloads.
Can Hudi work with AWS Glue?
Yes, Hudi can integrate with AWS Glue for schema management and ETL operations.
Is Hudi suitable for real-time data processing?
Yes, Hudi supports incremental processing and can be used in near-real-time data pipelines.
7. Flowchart of Data Ingestion Process
graph TD;
A[Data Source] -->|Ingest Data| B[Apache Hudi];
B --> C{Storage Type};
C -->|Copy-on-Write| D[Store as New Version];
C -->|Merge-on-Read| E[Store Immutable Base Files];
D --> F[Data Lake];
E --> F;