Case study
Multi-Tenant Elasticsearch Tuning for a Telecom Platform
How SquareShift tuned Elasticsearch for multi-tenant isolation and scale on a real-time telecom communications platform.
A market-technology and communications firm powering voice, messaging, analytics, and directory-service platforms at real-time, high-volume scale.
Its Elasticsearch 7.17.12 deployment needed deep tuning for ingestion throughput and secure multi-tenant isolation.
Best practices implemented, from alias-based routing to Terraform-defined infrastructure. Hot-warm-cold ILM policies and RBAC-based access applied per tenant. Introduced for cluster and ingestion monitoring.
Key servicesHow SquareShift delivered it.
The challenge
The client’s real-time communications platform — spanning voice, messaging, analytics, and directory services — ran Elasticsearch 7.17.12 at high ingestion volume, but indexing was inefficient and shard counts had crept up. Tenants weren’t properly isolated from each other, and retention followed manual, inconsistent ILM phases.
What we delivered
SquareShift rebuilt the platform’s ILM strategy around hot-warm-cold tiers, added ML-based anomaly detection to cluster and ingestion monitoring, and locked down tenant boundaries with role-based access control. The team implemented more than 30 best practices, including alias-based routing, dynamic scaling, Logstash multi-threading, field-level mapping control, and Terraform-managed infrastructure.
The payoff
Tenants are now properly isolated on a hot-warm-cold ILM structure with RBAC-scoped access, backed by ML anomaly detection across the cluster. More than 30 delivered practices — from routing to infrastructure-as-code — give the platform a repeatable foundation for scaling ingestion without cross-tenant interference.
Multi-tenant Elasticsearch fails quietly. One tenant's traffic spike degrades everyone else's queries unless hot-warm-cold ILM and RBAC-scoped access keep tenants properly isolated.
Cloud Modernization Practice Lead, SquareShift
Where this work sits