Why Delta Updates Are Not Recommended in OLAP Databases: A Performance and Efficiency Perspective
Delta Updates are not recommended in OLAP (Online Analytical Processing) databases due to the fundamental design and architecture of these systems, which are optimized for read-heavy analytical workloads rather than transactional operations. OLAP databases like ClickHouse, Apache Druid, or Amazon Redshift are built for high-speed querying and aggregation across large datasets. Here are the key reasons why Delta Updates are generally discouraged in OLAP databases:
1. Append-Only Data Model
OLAP databases are typically designed as append-only systems:
- Optimized for Data Ingestion and Aggregation: OLAP databases are designed to handle massive amounts of data being continuously ingested, with minimal changes to existing data. They are tuned for fast writes (inserts) and quick, large-scale reads, such as aggregations and analytical queries.
- Efficient with Append Operations: Append-only operations are easier to manage because they do not require data modification in existing rows, which would involve reorganizing storage, indexes, and memory structures.
- Delta Updates Conflict with the Design: Delta updates (modifying specific records in-place) contradict the append-only nature, as these systems are not designed to handle frequent updates to existing rows without significant performance degradation.
2. High Cost of Data Modifications
In OLAP databases, updates and deletes are expensive and inefficient for the following reasons:
- Columnar Storage Structure: OLAP systems store data in a columnar format, where data for each column is stored separately and compressed. This format is optimal for analytical queries (e.g., aggregations), but makes row-level updates complex and resource-intensive because multiple columns must be rewritten.
- Expensive Rewrites: To update a single row in a columnar storage system, the entire block containing that row may need to be rewritten, which incurs a significant cost in terms of disk I/O and processing time. This is especially problematic for large tables.
- Background Merge Process Overhead: Some OLAP databases (e.g., ClickHouse) use background merging processes (like MergeTree) to optimize and consolidate data. Delta updates add more workload to these processes, leading to higher resource consumption and longer merge times.
3. Optimized for Bulk Writes, Not Frequent Updates
OLAP databases are optimized for bulk data ingestion (large inserts):
- Batch Processing: OLAP systems thrive on bulk data loads and are typically used for periodic data ingestion (e.g., ETL jobs). Inserting new data in bulk is fast because it can be compressed and indexed as a single unit.
- Frequent Updates Reduce Efficiency: Introducing frequent updates via delta operations disrupts the natural workflow of OLAP systems. Each update triggers a need for compaction, re-indexing, and recalculating aggregates, which drastically slows down performance.
4. Lack of Transactional Consistency
OLAP databases are typically not designed to handle ACID (Atomicity, Consistency, Isolation, Durability)transactions, which are important for OLTP (Online Transaction Processing) systems:
- No ACID Guarantees: OLAP systems prioritize performance and speed over transactional consistency. Therefore, complex operations like delta updates, which require row-level locking and guarantees of isolation, are hard to implement and could result in inconsistencies.
- Concurrent Updates Are Problematic: OLAP systems are not designed for handling concurrent updates efficiently, which could lead to race conditions, inconsistencies, or conflicts when multiple users are updating the same records.
5. Focus on Aggregations and Reporting, Not Real-Time Updates
OLAP databases are optimized for analytical queries that focus on reporting, aggregations, and trend analysis:
- Designed for Reporting, Not Real-Time Updates: OLAP systems are typically used to run queries that scan and aggregate large datasets to provide insights, trends, and analytics. Real-time or near-real-time updates are not the primary focus.
- Historical Data Use Case: OLAP databases are better suited for analyzing large sets of historical data rather than for performing frequent updates to current data. Delta updates introduce unnecessary complexity into an architecture that is built for high-speed reads and aggregations.
6. Data Compaction and Merging Complexity
Most OLAP databases employ data compaction and merging strategies to manage data efficiently over time:
- Merging Processes: To handle large datasets efficiently, OLAP databases often rely on background processes (e.g., ClickHouse’s
CollapsingMergeTree,ReplacingMergeTree) to merge, compress, and reorganize data. Delta updates require additional merging and compaction, which introduces delays and increases resource consumption. - Fragmentation and Performance Degradation: Frequent updates lead to data fragmentation, requiring more frequent merges, which ultimately degrades query performance due to fragmented storage layouts.
7. Alternative Workflows for OLAP Systems
Given these constraints, OLAP systems typically use alternative workflows for handling data changes:
- Insert-Only Strategy: Instead of updating existing rows, new rows are often inserted with updated data, and the old data is either archived or logically deleted (soft delete). This works well with the append-only nature of OLAP databases.
- Eventual Consistency: Many OLAP systems rely on eventual consistency, where data changes are reflected gradually over time rather than immediately, as expected in transactional systems.
Conclusion:
Delta updates are not recommended in OLAP databases because of their append-only design, lack of ACID guarantees, high cost of row modifications, and the emphasis on bulk inserts and analytical query performance. OLAP systems are built to handle large-scale data aggregation and reporting tasks, and delta updates introduce unnecessary complexity and performance overhead. Instead, strategies like insert-only, soft deletes, and batch updatesare preferred to maintain high performance and efficient resource usage.
Mastering Performance Tuning in ClickHouse: Tips for Inspecting Statistics Objects
Enhancing Data Processing Workflows with Chained Materialized Views in ClickHouse
Troubleshooting ClickHouse Data Skew in Distributed Aggregation
