Time Series Database Benchmarks

Updated February 11, 2024

This page will gather ongoing time-series database benchmarks into one location.

  1. Top Benchmarks - Those that are open, comprehensive and thorough
  2. Benchmarks by Date - Sorted Chronologically

The top benchmarks section will focus on time-series databases that are fast, provide time-series joins, time window aggregations and data compression, rather than pull in more general solutions.

Top Time-series Benchmarks

To be considered a top benchmark, a combination of factors including repeatability, open-source, reputation, comprehensiveness, thoroughness were considered. This narrowed the top benchmarks to:

  1. benchmarking 4 queries on 1.1 Billion NYC Taxi and Uber Trips - by Mark Litwintschik's. Uses different hardware but thorough writeup of each database features.
  2. Clickbench - 30+ Databases with 42 queries benchmarked. Only includes open-source but highly reproducible.
  3. STAC-M3 - Non-open costly commercial benchmark
  4. H2O.ai - reproducible benchmarking of database-like operations in single-node environment.

Updated Feb 11, 2024

Updated Oct 4, 2023

A number of vendors have presented short demo videos for our Modern Databases for Finance series, you may find this useful to quickly see what different databases offer.

Updated Aug 14, 2023

The latest updates were all vendor customized benchmarks for ingestion, each of the vendors benchmarks showed themselves as the fastest at ingestion: questdb, GridDB, TDEngine.

Taxi Ride Benchmarks

Results reproduced from Mark Litwintschik's excellent article.
Mark benchmarked 4 queries against a 1.1 Billion NYC Taxi Trips data set. It had 51 columns was 500 GB in size when in uncompressed CSV format

SetupTotal Query Time
(lower = better)
kdb+/q & 4 Intel Xeon Phi 7210 CPUs1.0
ClickHouse, 3 x c5d.9xlarge cluster4.1
OmniSci & a 16" MacBook Pro4.3
Clickhouse on DoubleCloud, s1-c32-m1285.8
Redshift, 6-node ds2.8xlarge cluster8.0
Amazon Athena25.3
Presto 0.214 & 21 x m3.xlarge HDFS cluster29.4
Spark 2.4 & 21 x m3.xlarge HDFS cluster30.4
Vertica, Intel Core i5 4670K147.3
PostgreSQL 9.5 & cstore_fdw930.0
Spark 1.6, 5-node m3.xlarge cluster w/ S32158.0
Elasticsearch (heavily tuned)26.3


Clickbench is a large suite of benchmarks produced by clickhouse themselves. The focus is not time-series but a wide range of queries. They are being very transparent and open, i.e. on some queries they are beaten but the benchmarks only include open source choices.

System & MachineRelative time (lower is better)Note
ClickHouse (c6a.metal, 500gb gp2):×1.59
SelectDB (c6a.metal, 500gb gp2):×1.88
ClickHouse (m5d.24xlarge):×2.15
StarRocks (c6a.metal, 500gb gp2):×2.16
Redshift (4×ra3.16xlarge):×2.20
DuckDB (c6a.metal, 500gb gp2):×2.74
QuestDB (partitioned) (c6a.metal, 500gb gp2)†:×24.24
MariaDB ColumnStore (c6a.4xlarge, 500gb gp2)†:×59.27
TimescaleDB (compression) (c6a.4xlarge, 500gb gp2):×86.91
Druid (c6a.4xlarge, 500gb gp2)†:×150.50
PostgreSQL (c6a.4xlarge, 500gb gp2):×883.89NOT column oriented


STAC-M3 are highly commercial and results are not published or reproducible. Typically each vendor pays to perform a STAC-M3 benchmark and at the time they use the fastest available hardware humanly possible and a highly optimized configuration. making results extremely difficult to compare. They are however as far as we know, extremely thorough.

Below is a rough overview of the winners doing press releases by year:

System & MachineRelative time (lower is better)
2022 - Maykdb+ + Google Cloud improves up to 18x in latest STAC benchmark
2022 - FebruaryDDN and Shakti Announce Record Breaking Results on the STAC-M3 Benchmark for Financial Trading Applications
2020INFOWeka and KDB 3.6 claim 'Record-Breaking Results on 17 STAC-M3 “Tick Analytics” Benchmarks'
2014McObject's eXtremeDB McObject and Lucera Set Records for Market Data Analysis
2012McObject's eXtremeDB Financial Edition Sets Records in STAC-M3 Benchmark of Market Data Analysis

h20.ai Reproducible Benchmarks

Aims to benchmark various database-like tools popular in open-source data science. It runs regularly against very latest versions of these packages and automatically updates. They provide this as a service to both developers of these packages and to users. You can find out more about the project in Efficiency in data processing slides and talk made by Matt Dowle on H2OWorld 2019 NYC conference.

All Other Time-series Benchmarks by Date

Rows marked No in the below table mean they are produced by a vendor themselves and that you should assume they have chosen a benchmark to highlight their best possible performance. If you want a benchmark added please contact us.

2023-05-16 Vendor TSBS Benchmark 1M rows per sec loading on customized TSBS benchmark by QuestDB.
No Vendor chosen customized benchmark.
GridDB, QuestDB, TimescaleDB, ClickHouse
2023-05-02 Vendor TSBS Benchmark 300K rows per sec loading on customized TSBS benchmark by GridDB.
No Vendor chosen customized benchmark.
GridDB, QuestDB, TimescaleDB
2023-04-14 H2O.ai Database-like Ops Benchmark DuckDB updates existing H20.ai benchmark.
No Vendor chosen customized benchmark.
DuckDB vs Pandas vs Clickhouse
2023-02-20 Vendor TSBS Benchmark Ingestion and query performance using customized TSBS Benchmark by TDEngine.
No Vendor chosen customized benchmark.
InfluxDB vs TimescaleDB vs. TDengine
2023-02-15 Academic Research benchmarking specialized databases for high-frequency data
kdb N/A Paper says benchmark was to be open source but can't find it. Source requested from authors.
ClickHouse, InfluxDB, kdb+, TimeScaleDB
2023-01-25 ClickHouse vs PostgreSQL Yes Analyse billions of youtube video metrics.
Clickhouse 5-10x faster than postgres for analytic queries
ClickHouse, PostgreSQL
2023-01-17 SigNoz SigNoz is an observability platform based on ClickHouse. Here they perform 500GB log analysis mostly using aggregation queries. Note:
SigNoz / Clickhouse won but this is a No vendor self supplied benchmark.
ClickHouse, ElasticSearch, MinIO
2022-06-08 Academic Paper SciTS: A Benchmark for Time-Series Databases in Scientific Experiments and IoTs
Yes "Click-House supports very high ingestion rates up to 1.3 million records...ClickHouse significantly outperforms other evaluated databases in the speed of data queries and shows reasonably low deviation in query latency."
ClickHouse, InfluxDB, TimescaleDB, and PostgreSQL
2022-05-26 QuestDB Blog 4Bn rows/sec query benchmark: Clickhouse vs QuestDB vs Timescale
No Vendor chosen benchmark to emphasise a very particular scenario.
Clickhouse, QuestDB, Timescale
2022-04-05 KX Blog KX v DBOps Benchmark Results
No Vendor ran benchmark. Claims to have ran fastest 18/19 queries but no speed numbers published.
2022-04-02 data-sleek blog SingleStore vs. ClickHouse Benchmarks
"SingleStore offers a much stronger solution than ClickHouse. The performance of the queries when joining tables is obvious — queries were 3-186x faster."
ClickHouse, SingleStore
2022-04-01 Masters Thesis Evaluating ClickHouse as a Big Data Processing Solution for IoT-Telemetry ClickHouse, ElasticSearch, Loki
2022-03-14 Altinity Blog Evaluating Altinity ClickHouse vs Singlestore for loading 100b rows
No Vendor self-benchmark
ClickHouse, altinity, singlestore
2022-02-15 Gitlab CH Eval Gitlab APM ClickHouse evaluation - to evaluate ClickHouse as a horizontally scalable datastore for o11y data (metrics, logs, traces).
Chose ClickHouse
Clickhouse, CrateDB, MongoDB and TimescaleDB
2022-01-18 Apache Doris Apache Doris self-published TPC-H benchmarks - No Vendor-ran benchmark Apache-Doris
2022-01-18 DB - Clickhouse vs kdb+ - Yes Internal Deutsche Bank benchmark. Clickhouse fastest clickhouse vs kdb+ kdb, clickhouse
2021-08-27 No duckdb Database Sorting Benchmark
No Duckdb wins their own chosen benchmark, clickhouse close.
duckdb, clickhouse, hyper, pandas
2021-07-08 alibabacloud ClickHouse vs. Elasticsearch
Clickhouse. "Elasticsearch is excellent in search scenarios where only a few records are filtered out by the WHERE clause. However, in analysis scenarios of large-scale data ... ClickHouse will have better concurrency performance due to its excellent column-storage mode and vectorized computing..."
ClickHouse, CrateDB, Druid, Hyper, MonetDB, PostgreSQL, SparkSQL, TimescaleDB
2021-04 brandonharris ClickHouse vs. Redshift - Financial analysis
Clickhouse faster.
ClickHouse, Redshift
2021-04 TSBS Open Source Time Series Benchmark Suite - Started by TimeScaleDB
Frustratingly they never seem to have published public results.
Akumuli, Cassandra, ClickHouse, CrateDB, InfluxDB, MongoDB, QuestDB, SiriDB, TimescaleDB, Timestream, VictoriaMetrics
2021 InfluxDB No No Vendor selected benchmarks by influxDB themselves.
Influx have chosen weak databases to compare against. Frustratingly their results whitepapers require email signup to see. They do claim to be 5x faster than MongoDB.
Elasticsearch, Cassandra, MongoDB, OpenTSDB, TimescaleDB
2020 mgbench Yes Yes Open source query benchmarks for machine-generated log data by Andrew Crotty - Assistant Professor of Computer Science at Northwestern University.
ClickHouse, CrateDB, Druid, Hyper, MonetDB, PostgreSQL, SparkSQL, TimescaleDB
2020-09-08 Clickhouse vs Redshift No Performance for FinTech Risk Management. ClickHouse, Redshift
2019-04-10 Measuring vertical scalability No Measuring vertical scalability for time series databases in Google Cloud by CEO of VictoriaMetrics.
Expected result by vendor: "VictoriaMetrics provides the best vertical scalability for both data ingestion and querying. "
InfluxDB, TimescaleDB and VictoriaMetrics
XXXX No No db-benchmarks.com No They claim to be publishing fair tests but the entire site and github repo is produced by manticore a vendor. I find it particularly distasteful to say "Fair" but not mention they work directly for one of the companies. Hence why I'm not linking to their site. ClickHouse, Elastic, Manticore