Apache Pinot is a columnar OLAP database optimized for real-time analytics on massive datasets with sub-second query latency, while Elasticsearch is a distributed search and analytics engine designed primarily for full-text search, logging, and operational analytics. Pinot excels at numerical aggregations over billions of rows; Elasticsearch excels at text search and log analysis.
Pinot vs Elasticsearch — Side-by-side comparison on A Versus B
Choose Pinot if you need to run fast aggregation queries (COUNT, SUM, AVG) over billions of events with sub-second latency and have primarily numerical data—ideal for user analytics, ad-tech dashboards, and real-time metrics. Choose Elasticsearch if you prioritize full-text search, log aggregation, or need flexibility to search across text fields with rich query DSL and a larger ecosystem—better for security/compliance logs, application performance monitoring, and general-purpose search.
Columnar OLAP database for real-time analytics at scale
Open-source distributed search and analytics engine for logs, metrics, and traces with flexible deployment options.
Pinot vs Elasticsearch — Comparison Data
Structured comparison data for Pinot vs Elasticsearch with 169 attributes.
Annual Infrastructure Cost (1TB dataset)
Typical Query Latency (1B rows, GROUP BY)
Monthly Cost (100GB index, 1M queries/month)
Mean Time to Resolution (MTTR) Improvement
Total Cost of Ownership (3-year, 500GB/month ingestion)
Required Infrastructure Team Size (100 users)
Pinot vs Elasticsearch — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Pinot vs Elasticsearch.
When should I use Pinot instead of Elasticsearch?
Use Pinot when you have numerical, time-series data at scale (100B+ events/day) and need sub-second latency on aggregation queries. Elasticsearch is more general-purpose and simpler to operate, but Pinot's columnar design makes it 5-10x faster and more storage-efficient for pure analytics. If you primarily do full-text search or logging, stick with Elasticsearch.
Can Elasticsearch do real-time analytics like Pinot?
Elasticsearch can perform analytics, but it's not optimized for it. Query latency on billion-row datasets typically ranges 500-2000ms vs. Pinot's 50-500ms. Elasticsearch's strength is real-time log indexing and text search; if you need fast numerical aggregations at massive scale, Pinot is superior. For hybrid workloads, some teams use both.
What are the cost implications of choosing between them?
Pinot's 0.1-0.3x compression ratio means 5-10x lower storage costs at petabyte scale. However, Elasticsearch has lower operational complexity and requires fewer expert DevOps resources. At 10 PB scale, Pinot can save $500K-$1M annually in storage; at 100 GB scale, Elasticsearch is cheaper due to its simplicity and smaller cluster requirements.
Can I migrate from Elasticsearch to Pinot?
Direct migration is not straightforward since they serve different use cases. Elasticsearch data (inverted indices) maps poorly to Pinot's columnar format. Most teams that switch run both systems in parallel: Elasticsearch for logs/search, Pinot for analytics. If considering a switch, build a new Pinot cluster in parallel and backfill from your data source (Kafka, S3) rather than attempting data port.
Which has better community support and documentation?
Elasticsearch has a significantly larger community (68K vs 9.2K GitHub stars), more third-party integrations, and broader vendor support. Pinot's documentation is comprehensive but smaller community means fewer Stack Overflow answers. For enterprise support, Elastic offers commercial licenses; Pinot is backed by Apache and community support through LinkedIn/DoorDash (major users).