The journey to today’s version of QuestDB began with the original prototype in
2013, and we’ve described what happened since in a post published during
our HN launch last year. In the
early stages of the project, we were inspired by vector-based append-only
systems like kdb+ because of the advantages of speed and the simple code path
this model brings. We also required that row timestamps were stored in ascending
order, resulting in fast time series queries without an expensive index.
We found out that this model does not fit all data acquisition use cases, such
as out-of-order data. Although several workarounds were available, we wanted to
provide this functionality without losing the performance we spent years
We studied existing approaches, and most came at a performance cost that we
weren’t happy with. Like the entirety of our codebase, the solution that we
present today is built from scratch. It took over 9 months to come to fruition
and adds a further 65k lines of code to the project.
Here’s what we built, why we built it, what we learned along the way, and
benchmarks comparing QuestDB to InfluxDB, ClickHouse and TimescaleDB.
The problem with out-of-order data#
Our data model had one fatal flaw – records were discarded if they appear
out-of-order (O3) by timestamp compared to existing data. In real-world
applications, payload data doesn’t behave like this because of network jitter,
latency, or clock synchronization issues.
We knew that the lack of out-of-order support was a show-stopper for some users
and we needed a solid solution. There were possible workarounds, such as using a
single table per data source or re-ordering tables periodically, but for most
users this is inconvenient and unsustainable.
How should you store out-of-order time series data?#
As we reviewed our data model, one possibility was to use something radically
different from what we already had, such as including LSM trees or B-trees,
commonly used in time series databases. Adding trees would bring the benefit of
being able to order data on the fly without inventing a replacement storage
model from scratch.
What bothered us most with this approach is that every subsequent read operation
would face a performance penalty versus having data stored in arrays. We would
also introduce complexity by having a storage model for ordered data and another
for out-of-order data.
A more promising option was to introduce a sort-and-merge phase as data arrives.
This way, we could keep our storage model unchanged, while merging data on the
fly, with ordered vectors landing on disk as the output.
Early thoughts on a solution#
Our idea of how we could handle out-of-order ingestion was to add a three-stage
- Keep the append model until records arrive out-of-order
- Sort uncommitted records in a staging area in-memory
- Reconcile and merge the sorted data and persisted data at commit time
The first two steps are straightforward and easy to implement, and handling
append-only data is unchanged. The heavy commit kicks in only when there is data
in the staging area. The bonus of this design is that the output is vectors,
meaning our vector-based readers are still compatible.
This pre-commit sort-and-merge adds an extra processing phase to ingestion with
an accompanying performance penalty. We nevertheless decided to explore this
approach and see how far we could reduce the penalty by optimizing the heavy
How we sort, merge, and commit out-of-order time series data#
Processing a staging area in bulk gives us a unique opportunity to analyze the
data holistically. Such analysis aims to avoid physical merges altogether
where possible and perhaps get away with fast and straightforward
similar data movement methods. Such methods can be parallelized thanks to our
column-based storage. We can employ SIMD and non-temporal data access where it
makes a difference.
We sort the timestamp column from the staging area via an optimized version of
radix sort, and the resulting index is used to reshuffle the remaining columns
in the staging area in parallel:
The now-sorted staging area is mapped relative to the existing partition data.
It may not be obvious from the start but we are trying to establish the type of
operation needed and the dimensions of each of the three groups below:
When merging datasets in this way, the prefix and suffix groups can be persisted
data, out-of-order data, or none. The merge group is where more cases occur as
it can be occupied by persisted data, out-of-order data, both out-of-order and
persisted data, or none.
When it’s clear how to group and treat data in the staging area, a pool of
workers perform the required operations, calling
memcpy in trivial cases and
shifting to SIMD-optimized code for everything else. With a prefix, merge, and
suffix split, the maximum
liveliness of the commit (how susceptible it is to
add more CPU capacity) is
Optimizing copy operations with SIMD#
Because we aim to rely on
memcpy the most, we benchmarked code that merges
__MEMCPY as Angner Fog’s Asmlib
A_memcpy, in one instance and glibC’s
memcpy in the other.
The key results from this comparison are:
glibccould be slow and inconsistent on AVX512 for our use case. We
A_memcpydoes better because it uses non-temporal copy
memcpyis pretty bad.
memcpyperform well on CPUs below AVX512.
A_memcpy uses non-temporal streaming instruction which appear to work well
with the following simple loop:
The above is a memory buffer filled with the same 64 bit pattern. It can be
memset if all bytes are the same. It also can be written as
vectorized code which uses platform-specific
store_nt vector method, as seen below:
The results were quite surprising. Non-temp SIMD instructions showed the most
stable results with similar performance to
Unfortunately, benchmark results with other functions were less conclusive. Some
perform better with hand-written SIMD and some just as fast with GCC’s SSE4
generated code even when it is ran on AVX512 systems.
Hand-writing SIMD instructions is both time consuming and verbose. We ended up
optimizing parts of the code base with SIMD only when the performance benefits
outweighed code maintenance.
How often should data be ordered and merged?#
While being able to copy data fast is a good option, we think that heavy data
copying can be avoided in most time series ingestion scenarios. Assuming that
most real-time out-of-order situations are caused by the delivery mechanism and
hardware jitter, we can deduce that the timestamp distribution will be locally
contained by some boundary.
For example, if any new timestamp value has a high probability to fall within 10
seconds of the previously received value, the boundary is then 10 seconds, and
we call this boundary lag.
When timestamp values follow this pattern, deferring the commit can render
out-of-order commits a normal append operation. The out-of-order system can deal
with any variety of lateness, but if incoming data is late within the time
specified as lag, it will be prioritized for faster processing.
Comparing ingestion with ClickHouse, InfluxDB and TimescaleDB#
We saw the Time Series Benchmark Suite
(TSBS) regularly coming up in discussions about database performance and decided
we should provide the ability to benchmark QuestDB along with other systems.
The TSBS is a collection of Go programs to generate datasets and then benchmark
read and write performance. The suite is extensible so that different use cases
and query types can be included and compared across systems.
Here are our results of the benchmark with the
cpu-only use case using up to
fourteen workers on an AWS EC2
m5.8xlarge instance with sixteen cores.
We reach maximum ingestion performance using four threads, whereas the other
systems require more CPU resources to hit maximum throughput. QuestDB achieves
959k rows/sec with 4 threads. We find that InfluxDB needs 14 threads to reach
its max ingestion rate (334k rows/sec), while TimescaleDB reaches 145k rows/sec
with 4 threads. ClickHouse hits 914k rows/sec with twice as many threads as
When running on 4 threads, QuestDB is 1.7x faster than ClickHouse, 6.4x faster
than InfluxDB and 6.5x faster than TimescaleDB.
Because our ingestion format (ILP) repeats tag values per row, ClickHouse and
TimescaleDB parse around two-thirds of the total volume of data as QuestDB does
in the same benchmark run. We chose to stick with ILP because of its widespread
use in time series, but we may use a more efficient format to improve ingestion
performance in the future.
Finally, degraded performance beyond 4 workers can be explained by the increased
contention beyond what the system is capable of. We think that one limiting
factor may be that we are IO bound as we scale up to 30% better on faster
When we run the suite again using an AMD Ryzen5 processor, we found that we were
able to hit maximum throughput of 1.43 million rows per second using 5 threads.
This is compared to the
Intel Xeon Platinum that’s in use
by our reference benchmark
m5.8xlarge instance on AWS.
Adding QuestDB support to the Time Series Benchmark Suite#
We have opened a pull request
(#157 – Questdb benchmark support) in
TimescaleDB’s TSBS GitHub repository which adds the ability to run the benchmark
against QuestDB. In the meantime, readers may clone
our fork of the benchmark suite and run the
tests to see the results for themselves.
To add out-of-order support, we went for a novel solution that yielded
surprisingly good performance versus well-trodden approaches such as B-trees or
LSM-based ingestion frameworks. We’re happy to have shared the journey, and
we’re eagerly awaiting feedback from the community.
For more details, the
GitHub release for version 6.0
contains a changelog of additions and fixes in this release.