@Rocky What is a general recommended partitioning scheme for datetime partitions on s3 tables to starrocks materialized views to make incremental updates of partitions efficient. • How many partitions is too much? • Size of partitions • Can I do days, weeks, months? Etc • What column data types on both size to make sure it works effectively

To make incremental updates of partitions efficient for S3-backed tables (Hive, Iceberg, or Hudi catalogs) feeding into StarRocks Materialized Views (MVs), you should follow a strategy centered on Partition Alignment. Here are the specific recommendations based on your questions: 1. General Recommended Partitioning Scheme The most efficient scheme is to align the partition key of the Materialized View with the partition key of the base S3 table. * Alignment: If your S3 table (e.g., in Glue or Hive catalog) is partitioned by a column

dt

, your MV should also be

PARTITION BY dt

. * Detection: StarRocks automatically detects data changes at the partition level for Hive and Iceberg. When a specific partition on S3 is updated, StarRocks only refreshes that specific partition in the MV, rather than the entire dataset. * Partition Rollup: If your S3 table is partitioned by day, but you want a weekly or monthly MV, use

date_trunc

in the

PARTITION BY

clause of the MV. This allows for "many-to-one" partition mapping while still maintaining incremental updates. 2. How many partitions is "too much"? While StarRocks can handle thousands of partitions, there are practical limits for metadata management and scheduling: * Recommended Limit: Generally, try to keep the total number of partitions per Materialized View under 10,000 to 20,000. * Impact of Excess: Too many partitions (e.g., 100k+) can lead to high memory consumption on the Frontend (FE) during metadata synchronization and increase the overhead of the refresh scheduler. * TTL Strategy: Use the

partition_ttl

property in your MV definition to automatically drop old partitions, keeping your partition count stable. 3. Size of Partitions Partition size is a balance between parallelism and metadata overhead: * Ideal Size: Aim for partitions between 100 MB and 1 GB of data (after compression). * Small Partitions: If partitions are only a few MBs (e.g., hourly partitioning with low traffic), you will encounter the "small file problem" on S3, which slows down the MV refresh process due to excessive S3 HEAD/GET requests. * Large Partitions: If partitions are 10GB+, a single update to one row in that S3 partition will force StarRocks to re-process the entire 10GB for that MV partition. 4. Can I do days, weeks, months? Yes. * Days: Standard for most high-volume fact tables. * Weeks/Months: Better for summary/aggregate MVs. * Rollup Example: If your base table is partitioned by day (

report_date

), you can create a monthly MV like this:

sql
    CREATE MATERIALIZED VIEW mv_monthly
    PARTITION BY date_trunc("month", report_date)
    REFRESH ASYNC
    AS
    SELECT date_trunc("month", report_date), sum(revenue)
    FROM hive_catalog.db.s3_table
    GROUP BY 1;

Note: StarRocks will correctly identify which months need to be refreshed based on which days changed on S3. 5. Column Data Types To ensure the partition mapping works effectively: * Supported Types: Use

DATE

,

DATETIME

, or

STRING

(v3.1.4+) as the partitioning key. * Consistency: Ensure the data type on the S3 side (e.g., Parquet

DATE

or

TIMESTAMP

) matches the StarRocks column type. * Recommendations: * Use

DATE

for daily partitions to keep the metadata lean. * Avoid using

INT

(like

20231024

) for datetime partitions if you plan to use time-based functions like

date_trunc

, as it requires extra casting that can sometimes interfere with partition pruning or alignment. Best Practices for Efficiency *

partition_refresh_number

: Set this property (e.g.,

"partition_refresh_number" = "1"

) to control how many partitions are refreshed in a single batch. This prevents OOM (Out of Memory) errors if a large historical backfill occurs on S3.