Project Name
Ksolves Drives 35% Latency Reduction and 40% Storage Savings for Telecom via Cassandra Migration
A global telecom operator running large-scale AI-driven analytics workloads used Apache Cassandra 5.x as its core data backbone to store and process massive volumes of call detail records (CDRs), IoT device telemetry, and machine learning feature data.
Initially, the cluster was deployed on Kubernetes for its automation and scaling benefits. But as the AI workloads matured and data volumes exceeded 50 TB, the team began to encounter increasing performance unpredictability, compaction overhead, and rising cloud costs.
To meet the stringent latency and throughput requirements of their real-time AI pipelines, the company made a strategic decision to migrate Cassandra 5.x from Kubernetes to a dedicated on-premises infrastructure optimized for I/O-intensive workloads.
- Unpredictable Performance Under AI Workloads: Machine learning feature extraction and model-serving pipelines required consistent low-latency reads, which were affected by containerized I/O and shared volume contention.
- Rising Cloud Costs: Persistent volume storage and cross-zone data transfers incurred significant recurring expenses that scaled faster than workload growth.
- Limited Hardware Optimization: Kubernetes abstraction restricted the team from performing deep system-level tuning (NUMA pinning, I/O schedulers, JVM optimizations), critical for AI use cases.
- Operational Complexity: StatefulSets, operators, and persistent volume management increased the cognitive and operational load for a relatively stable workload.
- Data Residency and AI Compliance Requirements: For AI models trained on subscriber behavior data, regulatory frameworks demand on-prem data residency and strict access controls.
The engineering team designed a phased migration plan based on connectivity and workload sensitivity. Their primary goal was to achieve predictable, low-latency access for AI model inference and feature generation, without downtime or data loss.
-
Migration Strategy
Two paths were evaluated based on environment connectivity: -
Online Migration (With Connectivity):
- Added new on-premises nodes directly into the existing Kubernetes Cassandra 5.x ring.
- Used Cassandra’s native streaming and incremental replication to synchronize data online.
- Validated dataset integrity post-streaming, then gracefully decommissioned Kubernetes nodes.
-
Offline Migration (No Connectivity):
- Captured cluster-wide snapshots via nodetool snapshot.
- Transferred SSTables securely to on-premises nodes using rsync and reloaded them with nodetool import and sstableloader.
- For smaller AI feature datasets, performed selective CQL-based export/import using cqlsh COPY for faster setup.
-
Implementation Highlights
- Maintained version parity with Apache Cassandra 5.0 to ensure compatibility with new features like Storage-Attached Indexing (SAI) and Unified Compaction Strategy (UCS) used in their AI workloads.
- Tuned on-prem nodes with direct NVMe SSDs, optimized JVM garbage collection (G1GC), and configured NUMA-aware CPU pinning for consistent performance.
- Leveraged rack-aware topology with dual 10G bonded NICs to optimize replication throughput.
- Conducted post-migration data validation using hash comparisons and workload replay testing with NoSQLBench.
- Read Latency (P95), reduced from 15–20 ms to 9–11 ms, ≈35% faster reads.
- Write Latency: Reduced from 10–12 ms to 6–8 ms, ≈30% faster writes. Storage Cost (Annual) dropped from 100% baseline to 60% baseline, ≈40% cost savings.
- AI feature load stability improved from fluctuating under load to consistent across pipelines, ~25% more stable.
- Operational Complexity, simplified from StatefulSets, PVCs, and Operators to direct node management, is ~50% easier to manage.
- Enabled stable, low-latency model feature lookups for AI applications
- Achieved predictable I/O performance across pipelines
- Gained tighter control over data governance
- Reduced TCO with Cassandra 5.x running natively on optimized hardware
- Improved throughput for AI-powered analytics pipelines
By migrating Cassandra 5.x from Kubernetes to an on-premises deployment, the telecom operator achieved the perfect balance between scalability, control, and cost efficiency. The move enabled their AI systems to operate with faster data access, consistent performance, and regulatory compliance, all while simplifying day-to-day operations. In short. This migration transformed Cassandra 5.x from a cloud-managed workload into a high-performance AI data platform, unlocking 35% faster reads, 40% lower costs, and predictable scalability for the telecom operator’s next-generation AI initiatives.
Transform your telecom operations with expert Cassandra migration.
