Segment metadata

[spaghettifunk]

When working with segments and index files in a columnar storage system, there are several useful metadata that you can keep to enhance query performance and optimize data access. Here are some examples of useful metadata to consider:

1. Segment Metadata: Maintain metadata about each segment, such as segment ID, size, creation time, interval (start and end timestamps), and any other relevant information specific to your data. This metadata helps in segment selection, filtering, and pruning during query planning.

2. Column Metadata: Store metadata specific to each column within a segment, including column name, data type, encoding format, statistics (e.g., min/max values, distinct value count, cardinality), and null value information. This metadata assists in query optimization, predicate pushdown, and efficient column pruning.

3. Index Metadata: Keep metadata related to the index files, such as the column(s) they represent, the type of index (e.g., inverted index), statistics about the index (e.g., number of entries, memory footprint), and any relevant configuration details. This metadata aids in index selection, index-aware query optimization, and query planning.

4. Partitioning Metadata: If your data is partitioned into logical units (e.g., time-based partitions), store metadata about the partitioning scheme, partition keys, and boundaries. This metadata helps in partition pruning, reducing the amount of data accessed during query execution.

5. Compression Metadata: Track information about the compression techniques applied to each segment or column, including the compression algorithm, compression ratio, and any related configuration parameters. This metadata assists in efficient data decompression during query execution.

6. Data Access Patterns: Capture information about the frequency of column access, popular columns, or frequently executed queries. This metadata can be used to guide query optimization decisions, such as caching frequently accessed columns or prioritizing certain segments for data loading.

7. System Statistics: Monitor and store system-level statistics, such as overall data size, memory utilization, query latency, and other performance metrics. This metadata helps in capacity planning, resource allocation, and overall system optimization.

It's important to strike a balance between the level of metadata you maintain and the overhead it introduces.