Considering Apache Druid?
- ▸ Time-series OLAP at scale — observability or ad-tech workload with billions of events / day and the roll-up storage savings are the reason you're looking past ClickHouse and Pinot.
- ▸ Kafka indexing topology — supervisor tasks aren't auto-balancing the way you expected and segment compaction is becoming the operational bottleneck.
- ▸ Imply Polaris vs self-managed — the TCO model needs real numbers, and the team is debating whether the operational savings justify the managed-service premium.
JusDB Apache Druid specialists design, deploy, and operate real-time OLAP at scale. See Druid consulting →
Apache Druid, time-series OLAP at any scale.
In short: Apache Druid is an open-source, real-time OLAP datastore built for time-series-heavy analytical workloads. It separates ingestion, storage, and query into independently scaling tiers, uses roll-up pre-aggregation and time-partitioned segments, and ingests streaming data from Kafka for sub-second queries over event data.
Kafka indexing supervisors, roll-up pre-aggregation, time-partitioned segments, and the Coordinator + Overlord + Historical + Broker + Router topology — purpose-built for time-series OLAP at billions-of-events scale.
Apache Druid · historical+realtime
Time-partitioned segments · roll-up
0.00k
15ms
0k
0.1M
Event Ingestion
0.00M events/s[OK] segment: clicks_2026-06-19 handed off to historical
[INF] realtime: kafka indexing task consuming, lag 1s
[OK] compaction: merged 1,840 small segments → 12
[INF] coordinator: segment balance even across tiers
Representative fleet view · illustrative metrics
0M+
Events / sec Ingested
0.99%
Uptime SLA
0×
Median Query Speedup
0×
Roll-up Storage Win
Apache Druid service paths
Druid Consulting
Kafka indexing topology, roll-up strategy, segment granularity, Coordinator / Historical tier sizing, Imply Polaris vs self-managed economics — written advisory deliverables.
Druid vs Pinot
Side-by-side comparison — roll-up vs star-tree, Kafka indexing vs LLC, multi-tenancy models, Imply Polaris vs StarTree Cloud, when each one wins.
What we do
What we build with Apache Druid
From cluster design to production query tuning — end-to-end Druid expertise.
Real-Time Kafka Ingestion
Kafka indexing service auto-scales supervisor tasks across MiddleManagers; second-level freshness from topic to query-ready segments.
Roll-Up Pre-Aggregation
Destructive aggregation at ingestion time — 10-100x storage reduction for time-series workloads where raw rows aren't needed downstream.
Time-Partitioned Segments
Segments natively partitioned by time interval — query pruning, retention policies, and compaction all operate on time-aligned units.
Tier-Decoupled Architecture
Coordinator + Overlord + Historical + Broker + Router tiers scale independently — match infrastructure to actual workload shape.
Deep Storage + Hot Tiers
Deep storage on S3/HDFS/GCS plus hot Historical-node caching — predictable retention with cost-aware tiering.
Imply Polaris Operations
Managed-Druid SaaS with Pivot visualisation included — fast time-to-value when operational burden is the dominant cost.
Time-series OLAP performance
Druid expertise
for event-scale analytics
We tune roll-up dimensions and segment granularity, balance Kafka indexing supervisors, and right-size the Historical and Broker tiers so time-series queries return in sub-second time even as event volume grows into the billions.
Time-Series Performance
After tuning40×
Median speedup
20×
Roll-up storage win
Real cases
Queries we've transformed
6,000ms
90ms
Raw events stored — billions of un-aggregated rows
The fix
Enabled roll-up at ingest — 20× less storage, faster scans
4,800ms
70ms
1,800 tiny segments — broker fan-out overhead
The fix
Configured auto-compaction to merge into optimal segments
1,900ms
35ms
Repeated dashboard queries recomputed every time
The fix
Enabled broker result cache for hot time-series queries
0.00%
Cluster Uptime
<0s
Failover RTO
0s
Ingestion Lag
High availability
Always on. Tier-decoupled.
Druid's tiers fail over independently — Coordinator and Overlord run in active/standby pairs, Historicals serve replicated segments, and deep storage means any lost node is re-served without data loss.
Incident response
A supervisor-stall P1, handled in under 15 minutes.
When a Kafka indexing supervisor stalls and ingestion lag spikes, a named Druid engineer responds — not a ticket queue. We reset the supervisor, rebalance tasks, and clear the backlog online, with a blameless postmortem after.
Time-series dashboard p99 > 6s — analysts blocked
Named OLAP engineer in under 15 min, not a ticket queue
Raw events stored — no roll-up, 1,800 tiny segments
Enabled roll-up at ingest + auto-compaction policy
Storage 20× smaller, p99 6s → 90ms — total 14 min
Pre-Migration Assessment
Data warehouse / Pinot → Apache Druid
Estimated cutover window: < 10 minutes
Migration
Move to Apache Druid without the downtime
Pinot, ClickHouse or a homegrown time-series store → Druid. We design roll-up and segment strategy, backfill historical segments in parallel, stand up Kafka indexing supervisors, and cut over once query results reconcile.
FAQ
Apache Druid — common questions
Get started
Ready to evaluate Druid?
Book a 30-minute scoping call. We'll review your workload shape, the roll-up strategy, and the managed-vs-self-managed decision before any statement of work.
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