Project Name
How a Leading Telecom Provider Doubled Cassandra Compaction Throughput with UCS in Cassandra 5.x
A leading telecom operator running large-scale data analytics for call records, network telemetry, and IoT device data relied on Apache Cassandra to handle billions of time-series and transactional events daily.
While Cassandra provided excellent scalability and uptime, the operations team constantly battled compaction inefficiencies. With different workloads across tables, from fast-moving, write-heavy ingestion to read-heavy analytics, managing multiple compaction strategies had become a full-time challenge.
- Fragmented Compaction Management: The cluster used a mix of STCS, LCS, and TWCS based on the table type. Each required separate tuning and monitoring, leading to high operational overhead.
- Compaction Bottlenecks: On high-density nodes with multi-terabyte disks, compactions often became I/O bottlenecks, slowing down write throughput during peak data ingestion.
- Performance Inconsistency: Some tables favored write performance but suffered during reads; others optimized reads but increased disk amplification. The imbalance caused unpredictable query latency.
- Complex Maintenance: Switching compaction strategies or retuning parameters required major compactions, consuming time, CPU, and I/O, making it impractical in always-on telecom workloads.
With the release of Cassandra 5.x, the engineering team deployed the new Unified Compaction Strategy (UCS) across all keyspaces to bring flexibility, automation, and performance consistency under one strategy. Implementation Highlights:
- One Unified Framework: Replaced all legacy compaction strategies (STCS, LCS, TWCS) with UCS, ensuring consistent configuration and simpler operations across hundreds of tables.
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Adaptive Behavior: Tuned UCS parameters based on workload type:
- T4 for real-time data ingestion (tiered-like behavior for high write throughput)
- L10 for analytics tables (leveled-like for better read amplification control)
- Parallel, Sharded Compactions: Enabled base_shard_count to distribute compactions across multiple disks, improving throughput and reducing backlog under heavy I/O load.
- Live Adjustments Without Downtime: Fine-tuned UCS parameters dynamically as workload patterns shifted; no major compactions or downtime required.
- Operational Complexity: Previously, managing Cassandra required handling three different compaction types per table. With Cassandra 5.x and UCS, a single unified UCS approach simplifies management by 70%.
- Compaction Throughput: Legacy strategies relied on sequential compactions, whereas Cassandra 5.x with UCS uses sharded and parallel compactions, making the process twice as fast.
- Latency Stability: Under mixed workloads, latency used to fluctuate significantly. With the new setup, auto-adaptive mechanisms ensure consistent read and write latency.
- Maintenance Downtime: Full compactions previously required scheduled downtime, but with Cassandra 5.x and UCS, all adjustments can be performed with zero downtime.
- Resource Utilization: Resource usage across disks was uneven before. The unified UCS approach now distributes resources evenly, improving overall hardware efficiency.
- Low-Latency Performance: Achieved consistent performance and eliminated compaction slowdowns.
- Simplified Management: Streamlined database operations, reducing manual tuning efforts.
- Operational Efficiency: Freed the team to focus on optimization and capacity planning.
The Unified Compaction Strategy (UCS) in Cassandra 5.x helped the telecom company unify and simplify one of the most complex operational aspects of Cassandra. By adopting UCS, the team no longer had to pick between Leveled, Size-Tiered, or Time-Windowed compaction. Instead, they gained a single, tunable framework that adapts dynamically to changing workloads without sacrificing throughput or stability.
Simplify Operations and Boost Throughput by Upgrading to Cassandra 5.x UCS.
