JDD2015: Migrating to continuous delivery in the world of financial trading - David Genn
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MIGRATING TO CONTINUOUS DELIVERY IN THE WORLD OF FINANCIAL TRADING IG Group is a global leader in online retail trading. It has over 120,000 active users around the world, and serves over 4.5 million trades per month. IG has had an online presence since the early 2000s and has grown sizeable IT real estate to support its many users and activities. Like many large companies, IG has depended on monthly big bang release cycles with long regression periods to guarantee the quality and reliability of its software. In 2013 IG started to move towards a continuous delivery model. This allowed us to break away from an ever-increasing monthly software release, and to enable higher quality and stability by releasing small change sets into production more frequently. In the world of financial trading, uptime and reliability are carefully monitored by financial regulators around the globe. In this talk, we look at the approaches and techniques IG have used to move to a continuous delivery model. Their API layer serves upwards of 40k requests per minute and is connected to numerous web, mobile, and public API clients. We will explore the technical and organisational challenges faced along the way, as well as some of the unexpected benefits.
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JDD2015: Migrating to continuous delivery in the world of financial trading - David Genn
- 1. Migrating to continuous delivery in the world of financial trading David Genn @david_genn
- 3. Challenges What is Continuous Delivery? Key Principles Technical Approach Team Culture
- 13. New key metric: What is your cycle time?
- 15. Encouraging greater collaboration between everyone involved in software delivery in order to release valuable software faster and more reliably.
- 16. Challenges What is Continuous Delivery? Key Principles Technical Approach Team Culture
- 17. •Uptime SLAs •Regulation / Auditors •Legacy code •Complex cross-team projects
- 18. •Physical servers •Development team attitude •What about the code-freeze?
- 19. Challenges What is Continuous Delivery? Key Principles Technical Approach Team Culture
- 20. •Every commit is a release candidate •Trust your tests •Automate everything •Separate deployment from release
- 21. Challenges What is Continuous Delivery? Key Principles Technical Approach Team Culture
- 24. Master v1.0.0- SNAPSHOT 1. New feature branch 2. Developers commit to feature branch
- 25. Master v1.0.0- SNAPSHOT 1. New feature branch 2. Developers commit to feature branch 3. Pull request
- 26. Master v1.0.0- SNAPSHOT 1. New feature branch 2. Developers commit to feature branch 3. Pull request 4. Merge to master
- 27. Master v1.0.0- SNAPSHOT 1. New feature branch 2. Developers commit to feature branch 3. Pull request 4. Merge to master 5. Bamboo checkouts a new branch: release- 1.0.0
- 28. Master v1.0.0- SNAPSHOT 1. New feature branch 2. Developers commit to feature branch 3. Pull request 4. Merge to master 5. Bamboo checkouts a new branch: release- 1.0.0 6. If the build passes, a tag is created: 1.0.0
- 29. Master v1.0.0- SNAPSHOT 1. New feature branch 2. Developers commit to feature branch 3. Pull request 4. Merge to master 5. Bamboo checkouts a new branch: release- 1.0.0 6. If the build passes, a tag is created: 1.0.0 7. The new branch and tag are pushed to origin
- 30. Master v1.0.0- SNAPSHOT release-1.0.0 1.0.0 release-1.0.0-HOTFIX If a bug is found and it cannot be fixed by ‘rolling forward’, a hotfix branch can be created off the release branch.
- 32. Developer merges code Bamboo tags the code and builds it Runs automated tests Deploys to dark test environment Runs automated tests MANUAL STOP Flip dark to light in test MANUAL STOP Deploy to UAT dark
- 34. Feature flags
- 35. Challenges What is Continuous Delivery? Key Principles Technical Approach Team Culture
- 41. What metric do you use to measure your team’s success?
Notas del editor
- Thank you for coming to listen CD is ecoming an increasingly popular and common place term Part of the DevOps movement Talking about: Why CD is essential The challenges of implementing it in a FinTech company Some lessons from our real world implementation
- I’m David Genn Tech lead of our API team We provide web and mobile based platforms to allow retail customers to trade the financial markets We have ~120k customers across the globe Process in excess of 4 million trades a month
- For us to really understand why Continuous Delivery is something we need to take seriously we need to consider the principles behind how we build software The Lean movement has helped shape the way the most effective development teams work I’d like to recommend this book as a great starting point for understanding it “This is Lean” by Niklas Modig and Par Ahlstrom
- Book starts with an anecdote about seeing your doctor It tells the tale of a woman who wakes up and discovers a lump in her breast and is worried that she may have cancer She goes to her doctor Her doctor refers her to a specialist 2 weeks later she goes to see the specialist who tells her that she needs to have a biopsy 10 days after that she goes to see the surgeon who can take the biopsy The results are sent back to her original doctor who finally gives her a diagnosis 5 days later Total time to get a diagnosis: 29 days A second woman also wakes up one morning and discovers a lump She goes to a specialist walk in clinic where everyone is onsite who she could need to see She sees a nurse who gives her a preliminary check She immediately sees a specialist who sends hr down the corridor to have the biopsy taken She waits two hours whilst the biopsy results come back She is given her diagnosis Total time to get a diagnosis 4 hours
- The first process focussed on efficiently using resources The doctors and nurses were kept completely busy because they had full waiting rooms of people to see To ensure all the medical staff were used effectively, you need a separate appointment to see each specialist This is called Resource Efficiency The second approach focuses on the time it takes to get a diagnosis. This means having everyone available in one site that may be needed to get a diagnosis You have no guarantee that every medical specialist will be fully utilised but you do guarantee a quick diagnosis This is called Flow Efficiency
- In software development we are still focussed on efficiently using our resources. We want our development teams and our QA teams to be fully occupied This means we want everyone working on delivering as many features as we can Inevitably this means we build up large batches of features that need to be released
- This approach does efficiently use our development and QA teams resources However it means we inevitably have to go slower as more co-ordination is needed and there are more hand offs and queues between teams This is ‘smart motorway’ in the UK which adjust the speed limit depending on the volume of traffic The more traffic, the slower they have to go to prevent traffic jams It’s the same with software development – the more code being deployed, the more co-ordination you need and the slower you have to go
- Large releases of code need large amounts of infrastructure to support it We need to co-ordinate release into each environment We need to co-ordinate regression testing, code freezes, bug fixes etc We end up creating roles like Release Manager and Delivery Manager to keep things on track
- And of course, large releases of code are much riskier as so much is changing If one aspect of the release goes wrong, it can have a catastrophic failure on the other features being deployed
- Rather than being like those large container ships, we need to be more like Amazon They’ve started a 1 hour delivery service for certain goods in certain cities They’re focussing on how quickly they can get a product to their customer, rather than how many things are being delivered at any one time
- Amazon clearly don’t use large vehicles to do their Prime Now deliveries – they use cycle couriers These guys can’t carry very much, but they can deliver very quickly We need to deliver software more like a bike courier and less like a container ship
- Too often we measure a development teams effectiveness based on their velocity - this merely measures our resource efficiency To measure our flow efficiency we need to measure our cycle time – how long does it take to go from having an idea or seeing a problem to getting the solution into prod The time it takes to deliver an idea in to production is what gives you a competitive edge. It doesn’t matter how many projects you deliver per year if your competitor can deliver ideas to customers more quickly. It is your cycle time that will determine your success. Facebook deploy features every week Amazon deliver code into prod every 12 seconds Google do 2 billion deploys every week It’s no wonder that these companies dominate so successfully
- Continuous Delivery is a principle that has been around for almost 10 years It is a set of principles for helping teams deliver valuable software to production as soon as it’s ready and move away from the large batch release process This infographic is a summary of Jez Humble and Dave Farley’s book on Continuous Delivery
- Continuous Delivery in summary
- As a Fintech company we face a number of challenges that we’re overcoming to allow us to get the benefits of Continuous Delivery
- Uptime SLAs – our uptime is carefully monitored which is essential given we’re handing client’s money. Any changes to our release process need to guarantee we don’t impact our uptime. Regulation / Auditors – IG’s operations are regulated by a number of different financial regulators around the globe. The key thing they are concerned about is that we have the correct processes in place to understand and control the change to each environment. We need to work with this process to ensure we continue to meet these requirements. Legacy code – legacy code and monolithic applications can present significant technical challenges to achieving continuous delivery Complex cross-tem projects – this involves large amounts of co-ordination between teams and big-batch releases have often been used as a mechanism for co-ordinating the release of code. This approach no longer works if each team is deploying as soon as they’re ready
- Physical servers - Many companies that do CD successfully use the flexibility that the cloud offers them – the ability to provision new server programmatically We are tied to using our own data centres which means we have to build much of the infrastructure that you would get for free in the cloud Developer attitude – CD puts all the responsibility for releasing code on to the development team. If they’ve been used to finishing their development and letting others worry about testing and deploying it then moving to CD will require a shift in attitude What about the code freeze – the code freeze has always been used as a way of giving QA teams a stable, consistent environment to regression test the platform. This is no longer available if teams are deploying continually.
- Those are the challenges many companies face, ourselves included, when looking to adopt CD. Many companies use different technical approaches to implement CD but however you do it, there are some core principles
- Every commit is a release candidate – every time you merge to master, you need to consider that code ready to go to prod. Gone are the days of coding for 2-4 weeks and then taking a ‘cut’ which you merge to a Release branch. Every time you merge to master you are saying that you’re confident your code is ready to go. Trust your tests – if you’re deploying to prod frequently you can’t rely on long manual regression cycles, you need suites of automated tests that you trust and give you confidence that your app performs as expected. Automate everything – deploying frequently is only possible if it’s easy to do without making mistakes. This means you need to automate as much of the process as possible. Separate deployment from release – there are two risky parts to putting new software live – deploying the new binary and then making the new functionality available. In a raditional release process we do both stages together. In CD we want to separate them. We want to be confident that the new binary is behaving itself before we allow it to serve live traffic. We want to be able to test the new functionality before we switch it on
- Those are the principles we’ve focussed on. Here is an overview of how we’ve implemented it.
- Firstly, our Git branching model. As we’ve already mentioned, in CD, every commit to master is an RC
- Developers work on a feature branch
- 1 or more devs will commit to that branch
- When the feature is complete they raise a PR
- When that PR is approved it is merged directly to master
- This triggers a Bamboo build which creates a branch release-1.0.0
- If the build passes it creates a tag 1.0.0 and publishes the binary to Nexus
- The branch and tag are pushed to origin and the binary is deployed
- If a bug is found, you would typically fix it by following the same process – create a new feature branch and merge it to master, creating a new tag to be released. If this isn’t possible, you can create a hotfix branch off the deployed tag.
- Another principle of CD is the ability separate the deployment of a new application from the release of the new funcitonality
- 1-3 deploys per week during core hours QA and devs running deploys Done means in prod