Dremio and End-to-End Performance Management

Dremio has introduced several capabilities that inteliigently improve query performance across the data lakehouse. With minimal to no action from users, Dremio will reduce query latency, handle data maintenance tasks, and eliminate redundant compute jobs.

This article is a summary of three of these performance management features. Read on to learn how reflections accelerate popular queries, how Iceberg table management maintains efficient data access, and how end-to-end caching drops the waste of repeated data reads.

Reflections

Reflections are a query acceleration functionality unique to Dremio, that minimise data processing times and reduce computational workloads. These accelerate data lake queries by creating optimised Apache Iceberg tables from file-based datasets, delivering orders-of-magnitude performance improvements. 

Dremio will review user queries and match them against available reflections. When a match exists, and is calculated to improve query performance, Dremio rewrites the query to use the reflection rather than the underlying table. This process is automatic and does not require users to know which reflection is used or to rewrite existing SQL code.

Dremio supports several types of reflections, each designed to accelerate different query patterns:

• Raw: full datasets optimised by dropping unneeded columns, pre-partitioning, sorting, or distributing the data.
• Aggregation: precomputed group-by aggregations across selected dimensions and measures. 
• External: optimised data in another system used without duplicating data into Dremio’s environment.
• Starflake: pre-materialised relationships for join patterns across star and snowflake schemas.

Reflections began as manually generated tables, relying on users to identify possible optimisations and then to create and manage them. However, over the years reflections have evolved into a dynamic, fully-autonomous performance functionality. Three of the most important innovations have been:

• Live Reflections: Reflections automatically update as new data arrives, ensuring queries always run against the freshest data.
• Incremental Reflections: Instead of rebuilding the entire table, reflections are refreshed incrementally, either by adding new rows or reprocessing only the changed data partitions. This dramatically reduces cost and refresh speed.
• Autonomous Reflections: Dremio can automatically design, create, refresh, and even retire reflections based on observed query patterns. Users get dynamic query acceleration without lifting a finger.

Iceberg Table Management

Iceberg tables rely on metadata to track files, dataset versions, and schema details. Without active management, tables can accumulate small files, outdated snapshots, and unnecessary metadata entries that slow down query processing. 

To assist with table management Dremio provides two maintenance commands, which together can restore Iceberg tables to peak efficiency:

• OPTIMIZE: rewrites data and metadata files into the optimal size range by compacting small files together or by splitting oversized files. This reduces query planning time, lowers storage overhead, and improves scan efficiency.
• VACUUM: removes snapshots that are no longer needed and deletes the associated data and metadata files (manifest files, manifest lists, and partition stats). This cleans out stale historical data and keeps metadata at a manageable size.

Alternatively, Dremio’s Open Catalog will automatically optimise and cluster your tables, handling the chore of table management for you.

End-To-End Caching

Dremio’s end-to-end caching improves performance by reducing the amount of work required to answer repeated or predictable queries. Instead of recalculating results, replanning repeated queries, or rereading data from storage, Dremio will cache and reuse several artefacts. This works to shorten response times and lower the cost of running common workloads by reducing both compute and I/O.

Dremio implements several complementary layers of caching throughout the analytics workflow:

• Query plane cache: stores frequently accessed metadata and planning information, enabling Dremio to reuse parts of the query plan rather than building it from scratch. 
• Results cache: if a query is repeated and the underlying data has not changed, Dremio will return answers from the previous query.
• Columnar cloud cache (C3): keeps frequently accessed data in-memory on the Dremio cluster nodes to avoid the latency of remote storage and network data transfers. 

Summary

Dremio brings several performance techniques together to create a more predictable experience for users working with large analytic datasets. Reflections reduce the cost of common queries by preparing optimised data tables that all users can benefit from. Iceberg table management keeps growing and evolving datasets healthy over time, keeping scans efficient and data storage lean. And end-to-end caching shortens response times by keeping useful data close to your compute and avoids redundant workloads by retaining query results and reusing query plans. When combined, these capabilities reduce costs and give Dremio users an almost hands-free approach to maintaining fast and reliable query performance across their data lakehouse.