Data-lake optimization (AWS)

Optimizing a data lake on AWS S3 with Athena and Glue Data Catalog involves several best practices to ensure data is efficiently stored, queried, and managed. The combination of S3 for storage, Glue for data cataloging, and Athena for querying is powerful, but proper optimization is critical for performance, cost efficiency, and scalability.

Key Optimization Strategies:

1. Data Format Optimization:
   • Use Columnar Data Formats: Store data in columnar formats like Apache Parquet or ORC instead of row-based formats like CSV or JSON. Columnar formats reduce the amount of data scanned by Athena, significantly improving query performance and reducing costs.
     • Parquet and ORC are highly compressed, support efficient encoding, and enable selective reading of columns.

CREATE TABLE my_parquet_table (
    id STRING,
    name STRING,
    age INT
)
STORED AS PARQUET
LOCATION 's3://my-bucket/my-folder/';

Partitioning:

• Partition Data: Organize your data in S3 using partitioning to divide large datasets into smaller chunks based on one or more fields, such as date or region.
  • Example: Partition by year, month, day, or any frequently queried dimension.
  • This allows Athena to read only the relevant partitions, reducing the amount of data scanned.

Example Partition Structure:

s3://my-bucket/my-data/year=2023/month=09/day=11/
s3://my-bucket/my-data/year=2023/month=09/day=12/

Example Partitioned Table Creation:

CREATE EXTERNAL TABLE my_partitioned_table (
    id STRING,
    name STRING,
    age INT
)
PARTITIONED BY (year STRING, month STRING, day STRING)
STORED AS PARQUET
LOCATION 's3://my-bucket/my-data/';

• Manage Partitions with Glue: Use AWS Glue Data Catalog to manage and update partitions. You can use Glue crawlers or manually add partitions when new data is added.

Example of adding partitions:

ALTER TABLE my_partitioned_table ADD PARTITION (year='2023', month='09', day='11') LOCATION 's3://my-bucket/my-data/year=2023/month=09/day=11/';

Compaction:

• Consolidate Small Files: Avoid having too many small files in S3. Instead, compact data into larger files. Small files increase the number of requests and overhead for Athena queries, which can hurt performance and increase costs.
  • Ideal file sizes for S3 with Athena are typically between 128 MB and 1 GB.
  • Use tools like Apache Spark, AWS Glue, or AWS EMR to compact small files into larger, more query-efficient files.

Example Spark job for compaction:

df.coalesce(1).write.parquet('s3://my-bucket/my-folder/', mode='overwrite')

Data Compression:

• Use Efficient Compression: Use data formats that support efficient compression, like GZIP for CSV/JSON or Snappy for Parquet/ORC. This reduces the size of the data stored in S3, minimizing storage costs and the amount of data scanned during queries.
  • Athena supports a variety of compression codecs. For Parquet or ORC, Snappy is preferred for fast compression/decompression.

Example:

CREATE TABLE my_compressed_table (
    id STRING,
    name STRING,
    age INT
)
STORED AS PARQUET
TBLPROPERTIES ('parquet.compression'='SNAPPY')
LOCATION 's3://my-bucket/my-folder/';

Efficient Schema Design:

• Eliminate Redundant Columns: Only store the columns you need, especially in columnar formats like Parquet/ORC, where each column is stored separately. Avoid wide tables when unnecessary.
• Use Partition Keys Properly: Ensure that frequently filtered columns are part of your partitioning strategy. For instance, if you often query data by date, include the date as a partition key.

Optimize Glue Crawlers and Data Catalog:

• Efficient Glue Crawlers: Use Glue Crawlers to automatically discover new partitions and update the Glue Data Catalog, but optimize how often they run. Set them to run based on your data ingestion frequency to avoid unnecessary costs.
• Data Catalog Partition Indexing: Enable partition indexing in AWS Glue for large datasets with many partitions to speed up partition lookups during queries.
  • Partition indexing improves query performance when you have thousands of partitions by reducing the overhead of scanning partition metadata.

Query Optimization in Athena:

• Use Projection Pushdown: When querying Parquet or ORC, Athena only reads the columns you select in your query, which improves performance. Make sure to use SELECT statements that only query the necessary columns.

SELECT name, age FROM my_parquet_table WHERE year='2023';

Leverage Partition Filtering: Always include partition keys (e.g., year, month, day) in your queries. This allows Athena to scan only the necessary partitions.

SELECT * FROM my_partitioned_table WHERE year='2023' AND month='09';

• Avoid SELECT *: Avoid querying all columns with SELECT *. Instead, select only the required columns to minimize data scanning.

Cost Management:

• Monitor Athena Costs: Since Athena is priced based on the amount of data scanned, optimizing queries (like limiting columns and partitions) directly reduces costs.
• S3 Storage Classes: Use appropriate S3 storage classes for cost optimization. Use S3 Intelligent-Tiering or S3 Glacier for infrequently accessed data and S3 Standard for frequently accessed data.

Lifecycle Policies for S3:

• Implement Lifecycle Policies: Set up S3 lifecycle policies to move older, less frequently accessed data to cheaper storage classes like S3 Glacier or to delete data when it is no longer needed.

Example Lifecycle Policy:

• Move data to Glacier after 90 days.

{
  "Rules": [
    {
      "ID": "Move older data to Glacier",
      "Filter": {
        "Prefix": "my-data/"
      },
      "Status": "Enabled",
      "Transitions": [
        {
          "Days": 90,
          "StorageClass": "GLACIER"
        }
      ],
      "Expiration": {
        "Days": 365
      }
    }
  ]
}

Summary of Key Practices:

1. Use columnar data formats like Parquet or ORC for efficient storage and faster queries.
2. Partition data by commonly queried fields (e.g., date, region) to minimize data scanned during queries.
3. Compact small files into larger ones to improve query efficiency and reduce overhead.
4. Use compression to reduce storage and scan costs.
5. Optimize your schema by storing only necessary columns and using partition keys efficiently.
6. Set up efficient Glue Crawlers and enable partition indexing in the Glue Data Catalog for large datasets.
7. Optimize your Athena queries by selecting only the necessary columns and filtering by partitions.
8. Monitor and optimize S3 storage costs by using appropriate storage classes and lifecycle policies.

These strategies will improve performance, reduce costs, and scale efficiently when using AWS S3 as your data lake, Athena for querying, and Glue for data cataloging.