It’s been 2 years since I worked full time in kdb+ but people seem to always want to talk to me about kdb+ and where I think it’s going, so to save rehashing the same debates I’m going to put it here and refer to it in future. Please leave a comment if you want and I will reply.
Let’s first look at the use cases for kdb+, then consider the alternatives, then which I think will win for each use-case and why.
Use Cases
A. Historical market data storage and analysis. – e.g. MS Horizon, Citi CloudKDB, UBS Krypton (3 I worked on).
B. Local quant analysis – e.g. Liquidity analysis, PnL analysis, profitability per client.
C. Real-time Streaming Calcuation Engines – e.g. Streaming VWAP, Streaming TCA…
D. Distributed Computing – e.g. Margin calculations for stock portfolios or risk analysis. Spread data out, perform costly calcs, recombine.
Alternatives
Historical Market Data – kdb+ Alternatives
A large number of uses want to query big data to get minute bars, perform asof joins or more advanced time-series analysis.
New Database Technologies – Clickhouse, QuestDB.
Cloud Vendors – Bigquery / redshift
Market Data as a Service
Let me tell you three secrets, 1. Most users don’t need the “speed” of kdb+. 2. Most internal bank platforms don’t fully unleash the speed of kdb+. 3. The competitors are now fast enough. I mean clickbench are totally transparent on benchmarking..
Likely Outcome: – Kdb+ can hold their existing clients but haven’t and won’t get the 2nd tier firms as they either want cloud native or something else. The previous major customers for this had to invest heavily to build their own platform. As far as I’m hearing the kdb cloud platform still needs work.
Local Quant Analysis – Alternatives
Python – with DuckDB
Python – with Polars
Python – with PyKX
Python – with dataframe/modin/….
Now I’m exaggerating slightly but the local quant analysis game is over and everyone has realised Python has won. The only question is who will provide the speedy add-on. In one corner we have widely popular free community tools that know how to generate interest at huge scale, are fast and well funded. In the other we have a niche company that never spread outside finance, wants to charge $300K to get started and has a niche syntax.
Likely Outcome: DuckDB or Polars. Why? It’s free. People at Uni will start with it and not change. Any sensible quant currently in a firm will want to use a free tool so that they are guaranteed to be able to use similar analytics at their next firm. WIthout that ability they can only go places that have kdb+ else face losing a large percentage of their skillset.
Real-time Streaming / Distributed Computing
These were always the less popular cases for kdb+ and never the ones that “won” the contract. The ironic thing is, combining streaming with historical data in one model is kdbs largest strength. However the few times I’ve seen it done, it’s either taken someone very experienced and skillful or it has become a mess. These messes have been so bad it’s put other parts of the firm off adopting kdb+ for other use cases.
Likely Outcome: Unsure which will win but not kdb+. Kafka has won mindshare and is deployed at scale but flink/risingwave etc. are upcoming stars.
Summary
Kdb+ is an absolutely amazing technology but it’s about the same amazing today as it was 15 years ago when I started. In that time the world has moved on. The best open source companies have stolen the best kdb+ ideas:
Parquet/Iceberg is basically kdb+ on disk format for optimized column storage.
Apache Arrow – in-memory format is kdb+ in memory column format.
Even Kafka log/replay/ksql concept could be viewed as similar to a tplog viewed from a certain angle.
QuestDB / DuckDB / Clickhouse all have asof joins
Not only have the competitors learnt and taken the best parts of kdb+ but they have standardised on them. e.g. Snowflake, Dremio, Confluent, Databricks are all going to support Apache Iceberg/parquet. QuestDB / DuckDB / Python are all going to natively support parquet. This means in comparisons it’s no longer KX against one competitor, it’s KX against many competitors at once. If your data is parquet, you can run any of them against your data.
As many at KX would agree I’ve talked to them for years on issues around this and to be fair they have changed but they are not changing quick enough. They need to do four things:
Get a free version out there that can be used for many things and have an easy reasonable license for customers with less money to use.
Focus on making the core product great. – For years we had Delta this and now it’s kdb.ai. In the meantime mongodb/influxdb won huge contracts with a good database alone.
Reduce the steep learning curve. Make kdb+ easier to learn by even changing the language and technology if need be.
You must become more popular else it’s a slow death
This is focussing on the core tech product.
Looking more widely at their financials and other huge costs/initiatives such as AI and massive marketing spending, wider changes at the firm should also be considered.
One user suggested Deferred Sync. I’m not including it as I think the implementation is bad and encourages code that would be unsafe and dangerous. To get an idea of why, see this excellent article: https://journal.stuffwithstuff.com/2015/02/01/what-color-is-your-function/
QStudio is the best SQL IDE for data analysis. DuckDB is the best free database for OLAP analytical queries.
Together they provide a powerful desktop platform for data analysis.
Powerful Local Database – qDuckDB is now at the core of qStudio.
Save Locally – Convert a query result from any database to store it locally.
Parquet File Viewer – Click to open a parquet file on windows.
Parquet is the stored data standard of the future and works with everything.
It’s approaching 2 years since we launched Pulse and it’s a privilege to continue to listen to users and improve the tool to deliver more for them. A massive thanks to everyone that has joined us on the journey. This includes our free users, who have provided a huge amount of feedback. We are commited to maintaining a free version forever.
We want to keep moving at speed to enable you to build the best data applications.
Below are some features we have added recently.
Pulse enables authors to simply write a select query, then choose columns for group-by, pivot and aggregation. Users can then change the pivoted columns to get different views of the data. The really technical cool part is:
Unlike other platforms, Pulse does not attempt to pull back all data.
All aggregation of data is performed on the SQL server, making it really fast.
For kdb+, aggregation and pivoting occur on the kdb+ end using the common piv function.
As we have deployed Pulse at larger firms with more users, the backend databases began to become a bottleneck.
Polling queries to data sources can now be cached and results reused within the time intervals selected.
This means whether there are 1, 10 or 100 users looking at the same dashboard with the same variables, it will only query once, not separately for every user.
The java core feature qStudio relied on for license key authentication has been removed in java 17. If you are using a version of qStudio lower than 2.53 you MUST upgrade this year. Download qStudio now.
Old license keys and old versions of qStudio cannot work on java 17+ as the core java library is removed.
qStudio 2.53+ released 2023-08-14 will accept both old and new license keys and work on java 8/9/11/17 …. everything. – Download it now.
All keys provided from 2024-01-01 have been using the new key format. These keys start with QSV3.
We really wish 11+ years ago we hadn’t chosen this particular library but what can you do 🙂
Existing enterprising customers may be issued an old key for exceptional circumstances. If absolutely required, get in touch.
Pulse is specialized for real-time interactive data, as such it needs to be fast, very fast. When we first started building Pulse, we benchmarked all the grid components we could find and found that slick grid was just awesome, 60East did a fantastic writeup on how Slick grid compares to others. As we have added more features, e.g. column formatting, row formatting, sparklines…..it’s important to constantly monitor and test performance. We have:
Automated tests that check the visual output is correct.
Throughput tests to check we can process data fast enough
Manual tests to ensure subtle human interactions work.
Memory leak checks as our dashboards can be very long running.
Today I wanted to highlight how our throughput tests work by looking at our grid component.
HTML Table Throughput Testing
To test throughput we:
Use scenarios as close to our customers typical use cases as possible.
The most common query being a medium sized scrolling trade blotters with numerical/date formatting and row highlighting.
200 rows of data, scrolling 50 rows each update.
We use a subscription connection to replay and render 1000s of data points as fast as possible.
Video Demonstrating 21,781 rows being replayed as 435 snapshots taking 16 seconds = 27 Updates per second. (European TV updates at 25 FPS).
Update: After this video we continued making improvements and with a few days more work got to 40 FPS.
We then examine in detail where time is being spent. For example we:
Turn on/off all formatting, all rendering options.
Add/Remove columns
Change screen sizes
Change whether edit mode is on or table cells have been select (Off fact: selecting a cell makes the grid 30% slower to update)
Then we try to improve it!
Often this is looking at micro optimizations such as reducing the number of objects created. For example the analysis of how to format columns is only performed when columns change not when data is updated with the same schema. The really large wins tend to be optimizing for specific scenarios, e.g. a lot of our data is timestamped and received mostly in order. But those optimization are for a later post.
We just announced a unique event that gathers 4 of the newest, most advanced databases for Finance into 1 hour:
If you work on big data in Finance, this is your chance to get an overview of the rapidly changing database landscape. TimeStored will be organizing a free online presentation, each database company will present 10 minutes on what is unique to their solution. Bringing together the top new technologies together in one place.
Our latest product Pulse is for displaying real-time interactive data direct from any database. To get most benefit, the underlying databases need to be fast (<200ms queries). For our purposes databases fall into 2 categories:
Really really fast, can handle queries every 200ms or less and seamlessly show data scrolling in
All Other Databases. The 95%+.
It’s very exciting when we find a new database that meets that speed requirement. I went to the website, downloaded QuestDB and ran it. Coming from kdb+ imagine my excitement at seeing this UI:
Good News:
A very tiny download (7MB .jar file)
There’s a free open source version
They are focussed on time-series queries
Did I mention it’s fast
I wanted to take it for a spin and to test the full ingestion->store->query cycle. So I decided to prototype a crypto dashboard. Consume data from various exchanges and produce a dashboard of latest prices, trades and a nice bid/ask graph as shown below.
Good Points
It simply worked.
QuestDB chose to be PostgreSQL wire and query compatible. A great technical choice as:
It will work with many tools including Pulse without complication
Many people already know SQL. I’ve been teaching q/kdb for years and when people learn it, you can use it for absolutely amazing things that standard SQL is terrible at. However most people do not reach that level of expertise. By using standard SQL more people can reuse their existing knowledge.
They then added Time-series specific extensions ontop for querying, including:
“Latest on” – that’s equivalent to kdbs “last by”. It’s used to generate the “latest prices” table in the dashboard with a 1/5/15 minute lag.
QuestDB can automatically create tables when you first send data, there’s no need to send “Create Table …”. This was useful when I was tweaking the data layout from the crypto feeds.
At parts my SQL was rusty and I asked for help on their slack channel. Within an hour I got helpful responses to both questions.
Within a very short time, I managed to get the database populated and the dashboard live running. This is the first in a long time that a database has gotten me excited. It seems these guys are trying to solve the same user problems and ideas that I’ve seen everywhere. There were however some significant feature gaps.
Feature Gaps
No nested arrays. If I want to store bid/asks, I can only currently do it with columns bid1/bid2/bid3, no arbitrary length arrays.
Very limited window analytics. Other than “LATEST ON” QuestDB won’t let me perform analysis within that time window or within arrays in general.
I really missed my
`time xasc (uj/)(table1;table2)
pattern for combining multiple tables into one. For the graph I had to use a lengthy SQL UNION.
In general kdb+ has array types and amazingly lets you use all the same functions that work on columns on nested structures. I missed that power.
No security on connections. It seems security integration will be an enterprise feature.
Open Source Alternative to kdb+ ?
Overall I would say not yet but they seem to be aiming at a similar market and they are moving fast.
In fact, if you look at their architecture on the right, it’s obvious some of their team have used kdb+. Data is partitioned on date, with a separate folder per table and a column per file. Data is mapped in when read and appended when new data arrives.
In some ways this architecture predates kdb+ and originates from APL. It’s good to see new entrants like QuestDB and apache arrow pick up these ideas, make them their own and take them to new heights. I think kdb+ and q are excellent, I was always frustrated that it has remained niche while inferior technical solutions became massively popular, if QuestDB can take time-series databases and good technical ideas to new audiences, I wish them the best of luck!
Please leave any of your thoughts or comments below as I would love to hear what others think.
If you want to see how to setup QuestDB and a crypto dashboard yourself, we have a video tutorial:
Over the last few months, I’ve discussed grid components, aggregating and pivoting with a lot of people. You would not believe how much users want to see a good grid component that allows drill down and how strongly they hold opinions on certain solutions. I have examined a lot of existing solutions, everything from excel, to powerBI, Oracle, DuckDB, hypertree, grafana, tableau……. I think I’m beginning to converge these ideas and requests into a pivot table that will be a good solution for our users:
Like all of our work, it should be really really fast
It must work with Big Data
It should be Friendly
It must allow changing aggregations – e.g. Group by exchange OR group by exchange and sym
Allow pivoting some calculations – from one column to a breakdown in separate columns
It must work for all databases.
Well now the proposed interface looks like this:
Pulse - Pivot
A lot of the functionality inspiration should be credited to Stevan Apter and HyperTree. Ryan had seen HyperTree and loved the functionality and beautiful kdb only implementation. The challenge was to allow similar functionality for all databases while making it more accessible. We now have a working demo version.
If you love pivot tables and have never got to see your dream grid component come to fruition, we want to build it, so get in touch.
 
