--- title: "MongoDB vs. Tiger Data (TimescaleDB) | The Right Choice for Time-Series Data" description: "Discover why MongoDB hits an architectural dead end for time-series workloads. Learn how Tiger Data, the creators of TimescaleDB, delivers purpose-built performance, scalability, and SQL-native simplicity for your time-series data." url: "https://www.tigerdata.com/compare/mongodb" --- # Keep SQL. Keep your joins. Skip the schema drift. Tiger Data, from the creators of TimescaleDB, extends PostgreSQL for high-volume time-series and analytics. MongoDB is a flexible document database, but at high-ingest time-series scale, its query language, weak joins, and 16 MB document limits get in the way. TimescaleDB gives you standard SQL, hypertables, hybrid row-columnar storage, powerful compression, and real-time analytics in one database. ## Why not MongoDB MongoDB is fast to build on for flexible, document-shaped data. As workloads become high-ingest, with millions of time-series rows requiring analytics at scale, teams hit the document model's edges: no SQL, weak joins, schema drift, and 16 MB per-document limits. ### No SQL → a query language to work around MongoDB uses MQL and the aggregation pipeline, not SQL. BI tools, ORMs, and analysts built around SQL need adapters, and non-trivial analytics turn into multi-stage pipelines. ### Document model → joins are a workaround There are no foreign keys, and cross-collection joins run through $lookup in the aggregation pipeline. On normalized data this gets slow: in RTABench, MongoDB was the slowest system tested, with six queries timing out. ### Aggregation pipeline → rollups get complex Rollups and dashboards are built as multi-stage pipelines with a 100 MB per-stage memory limit and a 1000-stage cap. There are no Continuous Aggregates, so keeping pre-computed rollups fresh is on you. ### Hard limits → schema gymnastics at scale Documents are capped at 16 MB, and time-series buckets at 1000 measurements or 125 KB. You can't write to time-series collections inside a transaction, and their indexing is limited. ### Flexible schema → drift over time Schema-on-read is great early on, but without enforced structure, fields diverge across documents. Validation is a bolt-on, and analytics has to defend against inconsistent shapes. ### Where MongoDB fits well MongoDB suits flexible, document-shaped data and rapid iteration. It has a broad ecosystem, a managed cloud option, horizontal scale-out through sharding, and its own time-series collections and search and vector search. The trade-offs above apply mainly to relational, high-ingest time-series analytics. ## MongoDB vs Tiger Data for time-series Tiger Data makes Postgres powerful for time-series with fast, affordable, and familiar database features. ### Query language **MongoDB:** - MQL and the aggregation pipeline, not SQL - BI tools and ORMs built around SQL need adapters **Tiger Data:** - 100% standard SQL, no need to master a new query language - Works with existing drivers, ORMs, and BI tools ### Data relationships **MongoDB:** - Document model with no foreign keys - Cross-collection joins run through $lookup in the aggregation pipeline **Tiger Data:** - Native SQL joins, foreign keys, and constraints - Time-series and relational data in one database ### Analytics & rollups **MongoDB:** - Aggregation pipeline with a 100 MB per-stage memory limit and 1000-stage cap - No Continuous Aggregates for auto-refreshed rollups **Tiger Data:** - Built-in Continuous Aggregates with auto-refresh - Hypercore: Hybrid row and columnar storage for fast analytical queries - Native downsampling ### Scale & storage limits **MongoDB:** - 16 MB document cap; time-series buckets limited to 1000 measurements / 125 KB - No write to time-series collection inside transactions **Tiger Data:** - Hypertables with no per-row size ceiling, plus full transactional writes - Up to 95% compression, cutting storage footprint and cost - Tiered storage to low-cost object storage, still queryable in SQL ### AI & vector **MongoDB:** - Vector, full-text, and hybrid search are available across Atlas, Enterprise Advanced, and Community, but run as separate search nodes you deploy and keep in sync - Retrieval lives in the document model, not alongside relational joins **Tiger Data:** - Vector (pgvector, pgvectorscale) and BM25 keyword search (pg_textsearch) built into the Postgres engine - Hybrid search across embeddings, time-series, and relational data in one SQL query - No separate search cluster to deploy or keep in sync ## Real-time analytics benchmark RTABench measures real-time analytics on a normalized, multi-table schema — joins, selective filtering, and pre-aggregations, the way real applications query. Across the systems tested, MongoDB was the slowest, with six queries timing out; its document model isn't built for normalized, relational data. TimescaleDB, purpose-built for real-time analytics on relational schemas, completed the full query set. [See the RTABench methodology](https://rtabench.com/#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) ## Battle-tested at scale - Compression, joins, and materialized views out of the box - Full SQL compatibility - No sharding required ## Customer proof > The end result was excellent. Ingestion is faster than before, costs are lower than before, and we ended up ahead of our original timeline. > > — **Per Grapatin**, VP of Engineering ([Read more](https://www.tigerdata.com/blog/how-glooko-turns-3b-data-points-per-month-into-lifesaving-diabetes-healthcare-tiger-data)) > Adding a new diagnostic test with a new data-resolution requirement is now a simple configuration change, not a new service or migration plan as it was with MongoDB. > > — **Rajdeep Sharma**, VP of Product Development (migrated from MongoDB) ([Read more](https://www.tigerdata.com/blog/how-mechademy-cut-hybrid-digital-twin-infrastructure-costs)) > Everyone knows SQL at some level, even non-technical people. We can give someone in the data science team or customer support team access to TimescaleDB, and they'll figure out how to query the data, which wasn't the case with MongoDB. > > — **Jose Luis Ordiales Coscia**, Lead Software Engineer (migrated from MongoDB) ([Read more](https://www.tigerdata.com/blog/how-evergen-uses-tigerdata-to-scale-its-renewable-energy-monitoring-architecture)) > We rely on Timescale for everything now. It's super efficient, and we've reduced query load times from 30 seconds down to almost nothing. > > — **John Eskilsson**, System Architect (migrated from MongoDB) ([Read more](https://www.tigerdata.com/case-studies/edeva))