TimeStored Blog

New SQL Docs

We just launched a new sql documentation website: sqldock.com
to allow integration with Pulse / qStudio and docs more easily.
More updates on this integration will be announced shortly. 🙂

Pulse 2.0 Feature Sneak Peak – Markers

We have been working on version 2.0 of Pulse with a select group of advance users for weeks now. To give you a preview of one new feature, check out markers shown on the chart below. We have marker points, lines and areas.For example this will allow adding a news event to a line showing a stock price. This together with many other changes should be released soon as part of 2.0.

HTML Grid Live Update Performance

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:

1. Use scenarios as close to our customers typical use cases as possible.
2. The most common query being a medium sized scrolling trade blotters with numerical/date formatting and row highlighting.
   1. 200 rows of data, scrolling 50 rows each update.
3. 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.

Breakdown

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.

Modern Databases for Finance 2023

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.

Tuesday 24th October – 2-3PM UK

SIGN UP NOW

New Streaming Order Book Depth-map

We want to be the best finance streaming visualization solution. To achieve that, we can’t just use off the shelf parts, we have built our own market data order book visualization component from scratch, it’s only dependency is webgl. We call it DepthMap. It plots price levels over time, with the shading being the amount of liquidity at that level. It’s experimental right now but we are already receiving a lot of great feedback and ideas.

Faster Streaming Data
A lot of our users were capturing crypto data to a database, then polling that database. We want to remove that step so Pulse is faster and simpler. The first step is releasing our Binance Streaming Connection. In addition to our existing kdb streaming connection, we are trialling Websockets and Kafka. If this is something that interests you , please get in touch.

What skills does a financial software developer need?

The below network diagram is intended as an outline of the skill set required for a financial software developer.
Note:

• Most individuals should aim to have a strong core. Think of it like a pyramid, where the height is the strength of a skill. Core skills like general computing principles, probability, communication should be built “tall” and very strong.  peripheral skills such as python/monitoring will be weaker. An individual will typically only learn 1 or 2 niche areas strongly (T shaped)
• Notice the rough relative sizes of the areas. 55% computing, 10% math, 15% soft skills, 20% finance. This is intended to represent the rough allocation of effort.
  • If you only bring 95% techinical skills, you are going to waste time building the wrong thing, build something no one wants or build something useful but no one will know as you haven’t the soft skills to sell it.
• The management branch on the bottom left is optional.

Computing

Soft Skills

jq supports functions and new keywords.

Jq has now added support for:

1. Functions – {x+1}. Unnamed parameters beyond x don’t yet work so please name all your parameters.
2. Keyed table operations: xkey, 1!, 2!, keys, value.
3. New keywords supported: in, distinct, inter, except, rank, sv, vs, sum, prd, xlog.
4. Improved compatibility and support of: null, avg, var, iasc, upper, lower, fills, fill, ^, sublist, prds, sums.

The added keywords in most cases will only support the most common types and arguments.
Mixed lists in particular are not handled well by most keywords but we will continue to improve.

Exact Contents of Our Online KDB+ Training Course

We often get asked what is in our online training course.
We do describe this on the course page and in a PDF to be be totally thorough here’s a screenshot of our full listing:

kx kdb – 2019 in Review – Changes

Shakti

The biggest shakeup in the KDB world was Arthur Whitney, the founder of KX and creator of KDB selling his stake in KX and moving on to creating a new version of the K language called Shakti. “Shakti merges database, language, connectivity and stream processing into one powerful platform “.  So far it appears to overlap heavily with kdb functionality, adding further cryptographic features, while not yet supporting on-disk storage.

KDB Version 3.7 Changes:

• App Direct Mode – give users control over Intel Optane DC Persistent Memory.
• Multi-Threaded Primitive Operations
• Data at Rest Encryption.

KDB Version 3.6 Changes:

• Websocket – Improvements and bugfixes
• Speed Improvements
  • When attributes present use them more often.
• Improved Error Reporting
  • Broken or closed handles report their number
  • Fatal memory errors log a timestamp

FD/KX Products:

• Kx Dashboards – Expanded chart options available
• Enterprise Platform – SAML/GIT integration
• Kx Developer free to all Kx users, and Kx Analyst free to enterprise users.
• Kx Machine Learning Toolkit
• AxLibraries and Q Build Utilities

kx kdb – 2018 in Review – Changes

kdb Version 3.6 Changes:

• Enums and linked columns now use 64 bit indices
  • This is a disk-format change, i.e. newly saved data will NOT be backwards compatible.
  • 3.6 will be able to read data in the old format
• AnyMap – Mapped Nested Types
  • Ability to save unmappable compound objects with >2 billion elements
  • Mapped list elements can be of any type and are data remains mapped NOT copied to heap.
    • Symbols are automatically enumerated against a file with three ###s in the name.
• Deferred Response – -30!x Allows a deferred response to a sync query. In practice it is difficult to use correctly.
• New Functions:
  • .Q.hg – HTTP get allows retrieving web page as a list of strings.
  • .Q.dtps/.Q.dpfts added to allow specifying the enum domain
  • .Q.sha1 – SHA-1 encode text
  • .Q.ts – Allows timing a function call similar to apply “.”.
  • xcol – Now supports dictionary to remap column names
  • -27! to allow formatting similar to .Q.d
  • .j.jd – Allows specifying dictionary of options when calling json serialization.
  • .Q.btoa – Base 64 encode
  • .Q.hp – HTTP Post – .Q.hp[url;mimeType;query]
• Performance Improvements on
  • grouping
  • filtering
  • particularly when attributes present
• SSL – Improvements and bugfixes
• WebSockets – Improvements and bugfixes