Columnar memory format stores data tables as columns rather than rows. This allows for column-level compression and query processing algorithms that read only required columns efficiently.
Columnar storage is optimized for analytics workloads as opposed to row-oriented databases suited for transactions. It reduces I/O and improves cache usage by accessing homogenous column values.
Columnar formats like Apache Parquet have become popular for big data analytics. Columnar engines only load relevant columns for queries into memory. This provides performance gains over scanning all data.
Column stores integrate well with vectorized processing, compression schemes like RLE, and innovations like Apache Arrow for efficient analytical pipelines. Columnar engines like Apache Arrow DataFusion are optimized for incremental processing and distributed tracing.
Column values from each column are stored sequentially. Similar data types in a column are compressed. Analytics queries read only relevant columns sequentially.
Columnar databases use tight compression like run-length, dictionary, delta encoding on sorted column data. Query performance is enhanced by reduced I/O.
A columnar memory layout provides order-of-magnitude analytic query speedups and compression vs row-based formats. Columnar databases like Vertica, Parquet, ORC, Optimized Row Columnar (ORC) are widely used in data warehousing, analytics pipelines and big data.
Modern processors access columnar data efficiently using vectorized execution. Columnar storage helps tame growing data volumes cost-effectively.
Row oriented databases are better suited for high-volume transactional workloads requiring fast point lookups and frequent writes.
Common compression techniques include run-length, dictionary, delta encoding, and exploiting data type semantics. Adaptive and data-dependent schemes further improve compression.
It reduces I/O through tight compression. Scans read only needed columns. Better cache locality, vectorization, partitioning and compression all enhance performance.
While analytics queries are faster, point lookups and updates are slower. Amortized write cost can be higher in some cases. There is storage overhead of duplicated keys.
Apache Arrow is a cross-language development platform for in-memory data, specifying a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations.
Read more ->Apache DataFusion is an extensible, high-performance data processing framework in Rust, designed to efficiently execute analytical queries on large datasets. It utilizes the Apache Arrow in-memory data format.
Read more ->A data processing engine is a distributed software system designed for high-performance data transformation, analytics, and machine learning workloads on large volumes of data.
Read more ->
