Data Lake Partitioning

Data Lake Partitioning is a crucial optimization technique used to organize data within tables stored in Data Lakes and Data Lakehouses. It involves physically arranging the data files into separate directories (or logical groups managed by a table format) based on the values of one or more specified partition columns (e.g., date, region, customer category).

The primary purpose of partitioning is to enable partition pruning, allowing query engines to significantly improve performance by skipping the reading of irrelevant data files that do not match the query's filter criteria.

The Problem: Scanning Massive Datasets

Queries on large data lake tables often include filters on specific columns (e.g., 'WHERE event_date = '2023-10-26'' or 'WHERE country = 'US''). Without partitioning, a query engine would potentially have to:

1. List all data files belonging to the table (which could be thousands or millions).
2. Read metadata for (or even parts of) each file to determine if it might contain relevant data.
3. Scan large amounts of irrelevant data only to discard most of it based on the filter condition.

This full scan approach becomes extremely slow and costly on terabyte- or petabyte-scale tables.

How Partitioning Works

1. Partition Key Selection: One or more columns are chosen as partition keys. The choice of keys is critical and should align with common query filter patterns. Good partition keys have relatively low cardinality (not too many unique values) but effectively divide the data into reasonably sized, query-relevant chunks.
2. Data Organization: When data is written to the table, it is physically organized based on the partition key values.
   • Traditional (Hive-style): Data files are placed into directories named according to partition values (e.g., 's3://bucket/table/event_date=2023-10-26/country=US/').
   • Open Table Formats (Iceberg, Delta, Hudi): These formats manage partitioning more abstractly. They track which data files belong to which partition values in their metadata layers (e.g., Iceberg's manifest files). While they might still use directory structures resembling Hive-style partitioning, the metadata layer is the source of truth, offering more flexibility.
3. Partition Pruning (Query Time): When a query includes filters on partition key columns (e.g., 'WHERE event_date = '2023-10-26''), the query engine uses the partitioning information (either from directory paths or, more efficiently, from the table format's metadata) to identify only the specific partitions (and their associated data files) that could possibly contain matching data. All other partitions and their files are skipped entirely, dramatically reducing the amount of data that needs to be listed, accessed, and scanned.

Challenges with Traditional Partitioning

Hive-style directory-based partitioning faced several challenges:

• Partition Discovery: Query engines often had to perform expensive file system 'list' operations to discover available partitions.
• Partition Evolution: Changing the partitioning scheme (e.g., from daily to hourly) required rewriting the entire table.
• Too Many Partitions: Partitioning by high-cardinality columns could lead to an explosion in the number of directories and small files, negating performance benefits.
• Hidden Partitioning Complexity: Implementing partitioning based on transformed values (e.g., 'month(timestamp)') required users to know the transformation function when writing and querying.

Partitioning in Open Table Formats (e.g., Apache Iceberg)

Open Table Formats like Apache Iceberg significantly improve upon traditional partitioning:

• Metadata-Driven Pruning: Partition information is stored efficiently in metadata files (manifests), eliminating the need for slow directory listings. Engines prune partitions by reading metadata.
• Partition Evolution: Iceberg allows changing the partitioning scheme over time without rewriting old data. The table metadata tracks the different partitioning schemes used throughout the table's history.
• Hidden Partitioning: Iceberg supports defining partitions based on transformations (e.g., 'years(ts)', 'months(ts)', 'days(ts)', 'hours(ts)', 'bucket(N, col)', 'truncate(L, col)') applied to source columns. Users query using the original columns, and Iceberg automatically handles the mapping and pruning based on the partition definition. This hides physical layout complexity.
• Partition Value Statistics: Metadata stores statistics about partition values within manifests, further aiding query planning and pruning.

Key Benefits of Partitioning

• Massively Improved Query Performance: Reduces the amount of data scanned by orders of magnitude for queries filtering on partition keys.
• Reduced Query Cost: Lower data scanning translates directly to lower costs on cloud platforms where users pay for data scanned.
• Efficient Data Management: Provides a logical structure for organizing large datasets.

Data Lake Partitioning and RisingWave

When RisingWave sinks data into data lake tables, particularly using the Apache Iceberg sink, partitioning is a key consideration:

• Defining Partitioned Sinks: When creating an Iceberg sink in RisingWave, users can specify partitioning schemes compatible with Iceberg. RisingWave will then write output data into the correct partitions of the target Iceberg table.
• Performance Impact: The performance of downstream queries (whether from RisingWave itself reading the Iceberg table later, or from other engines like Spark or Trino) against the Iceberg table created by RisingWave heavily depends on choosing an appropriate partitioning strategy.
• Write Considerations: Writing to highly granular partitions from a streaming sink might still lead to small files within each partition, reinforcing the need for subsequent Data Lake Compaction.

Choosing effective partition keys based on expected query patterns is crucial when configuring RisingWave to sink data into partitioned lakehouse tables.