Version Control meets Database Control
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This joint webinar for DBmaestro (www.dbmaestro.com)and Delphix discuss the synergy between Delphix’s Database Virtualiztion and DBmaestro’s Database Enforced Change Management solutions. The session discuss the challenges in database development and show in practice how Database Enforced Change Management and Database Virtualization work together to create a version control, branching and merging method that addresses these challenges.
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Version Control meets Database Control
- 1. DBmaestro Introductions Webinar: Version Control Meets Database Control
- 2. Presenters Kyle Hailey @kylehhailey • Technical Evangelist at Delphix • Oracle ACE, member of the OakTable Network Uri Margalit @UriMargalit • • Director, Product Management Presenter at world-wide conferences: ODTUG, ilOUG, etc…
- 3. Before we start • You will be on mute for the duration of the event • We are now talking so please type a message in the Questions box in the Control Panel if you can’t hear us (please check your speakers and GoToWebinar audio settings first) • There will be a Q+A session at the end but please feel free to type your questions in the Questions box in the Control Panel in advance • A recording of the full webinar will be put up online
- 4. About Delphix • • • • Founded in 2008, launched in 2010 CEO Jedidiah Yueh (founder of Avamar: >$1B revenue)) Based in Silicon Valley, Global Operations 10% of Fortune 500
- 5. About DBmaestro • • • Founded in 2008 Headed by Yariv Tabac and Yaniv Yehuda Headquartered in Israel
- 6. Version Control meets Data Control Kyle Hailey & Uri Margalit
- 7. The Business Need 80% More than 50% of unplanned downtime is due to Change of this, half is due to human errors 40% of changes FAIL Copyright@2008, Juniper Networks, Inc.
- 9. Dealing with Risk Smaller and more focused changes are easier to manage (Agile…) Automation of repeating tasks lowers risk of (human) error Development and Operations should work in synergy (DevOps)
- 10. Source Control – Standard De Facto Common version control tools: GitHub SVN Perforce TFS RTC VSS
- 11. The Database Challenge • • • • • • • The Database is a crucial part of the Application — Schema Structure — PL/SQL Code — Lookup Content The Database is a central resource Business Data Must be preserved The Database is not native to traditional version control Objects are not files on a file system How can we manage Content? How can we branch a Database?
- 12. Tradeoff: Speed, Quality, Cost
- 13. Good, Cheap, Fast : choose two Good Cheap Fast
- 14. What We’ve Seen 1. 2. 3. 4. 5. Inefficient QA: Higher costs of QA QA Delays : Greater re-work of code Sharing DB Environments : Bottlenecks Using DB Subsets: More bugs in Prod Slow Environment Builds: Delays
- 15. 1. Inefficient QA: Long Build times Build QA Test Build Time 96% of QA time was building environment $.04/$1.00 actual testing vs. setup
- 16. 2. QA Delays: bugs found late require more code rework Build QA Env Sprint 1 Sprint 2 QA Build QA Env QA Sprint 3 X Bug Code Cost To Correct Delay in Fixing the bug Software Engineering Economics – Barry Boehm (1981)
- 17. 3. Full Copy Shared : Bottlenecks Frustration Waiting Old Unrepresentative Data
- 18. 4. Subsets : cause bugs
- 19. 4. Subsets : cause bugs The Production ‘Wall’ Classic problem is that queries that run fast on subsets hit the wall in production. Developers are unable to test against all data
- 20. Data 5. Slow Environment Builds: 3-6 Months to Deliver Data Developers Management Submit Request Approve Request $$ (2 Weeks) Approve Request $$ (2 Weeks) Approve Request $$ (1 Week) (2 Days) DBA System Admin (3 Days) (3 Days) Disk Capacity? Storage Admin (3 Days) Begin Work …….1-2 Weeks of Approvals, Delays, and Provisioning…… Request Additional Storage? File System Configured? Provision Capacity 20 Coordinate Replication w/ Infrastructure Configure LUNS & Build File System ReParameterize & Configure DB (3 Days) Mount Recovery DB to Specific PIT
- 21. 5. Slow Environment Builds: culture of no
- 22. What We’ve Seen 1. 2. 3. 4. 5. Inefficient QA: Higher costs QA Delays : Increased re-work Sharing DB : Bottlenecks Subset DB : Bugs Slow Environment Builds: Delays
- 23. Poll Which of the following have you run into at your organization? 1. Inefficient QA driving up costs 2. QA Delays causing increased re-work of code 3. Sharing DB causing development bottlenecks 4. Subset DB database in development and QA leading to bugs in production 5. Slow Environment Builds causing project delays
- 24. CIO Magazine Survey: 60% Projects Over Schedule and Budget Data is the problem Solve the data problem. TODAY.
- 25. UNLOCK YOUR DATA
- 26. 99% of blocks are identical Clone 1 Clone 2 Clone 3
- 27. Thin Clone Clone 1 Clone 2 Clone 3
- 29. Three Physical Copies Three Virtual Copies
- 31. Install Delphix on x86 hardware Intel hardware
- 32. Allocate Any Storage to Delphix Allocate Storage Any type
- 33. One time backup of source database Production Instance Database File system
- 34. DxFS (Delphix) Compress Data Production Instance Database File system Data is compressed typically 1/3 size
- 35. Incremental forever change collection Production Instance Database Changes Time Window File system • Collected incrementally forever • Old data purged
- 36. Typical Architecture Production Instance Database File system File system QA UAT Instance Instance Instance Database Database Database File system File system File system File system File system File system Development
- 39. Three Core Parts Development Production Virtual Database Instance Instance 3 1 Time Window 2 1. Source Syncing 2. Storage (DxFS) 3. Self Service
- 40. Fast, Fresh, Full Production Instance Virtual Database Instance Database File system Time Window
- 41. Free Instance Virtual Database Instance Virtual Database Production Instance Instance Virtual Database Database File system Time Window
- 42. Branching to QA Production Virtual Database Instance Instance Instance Virtual Database Database File system Time Window Dev QA
- 43. Self Service
- 44. What We’ve Seen With Delphix 1. 2. 3. 4. 5. Efficient QA: Low cost, high utilization Quick QA : Fast Bug Fix Every Dev gets DB: Parallelized Dev Full DB : Less Bugs Fast Builds: Fast Dev, Culture of Yes
- 45. 1. Efficient QA: Lower cost Build QA Test Build Time B u i l d T i m e QA Test 1% of QA time was building environment $.99/$1.00 actual testing vs. setup
- 46. 2. QA Immediate: bugs found fast and fixed Build QA Env Sprint 1 X Sprint 2 Build QA Env Sprint 3 Bug Code QA Sprint 1 QA QA Sprint 2 X Bug Code Sprint 3 QA
- 47. 3. Private Copies: Parallelize
- 48. 4. Full Size DB : Eliminate bugs
- 49. 5. Self Service: Fast, Efficient. Culture of Yes! Developers Management Submit Request Approve Request $$ (2 Weeks) Approve Request $$ (2 Weeks) Approve Request $$ (1 Week) (2 Days) (3 Days) DBA System Admin Storage Admin (3 Days) Disk Capacity? (3 Days) Begin Work …….1-2 Weeks of Approvals, Delays, and Provisioning…… Request Additional Storage? File System Configured? Provision Capacity Coordinate Replication w/ Infrastructure Configure LUNS & Build File System ReParameterize & Configure DB (3 Days) Mount Recovery DB to Specific PIT
- 50. What We’ve Seen With Delphix 1. 2. 3. 4. 5. Efficient QA: Low cost, high utilization Quick QA : Fast Bug Fix Every Dev gets DB: Parallelized Dev Full DB : Less Bugs Fast Builds: Fast Dev, Culture of Yes
- 52. Challenges of Development & Release to Operation Release Management Change Management Development Organizing the development of changes • Code • Database • Configuration • Metadata • Work Items Test/ Staging/ UAT Moving just the right components needed for the release • Release Approved Items Production Enabling safe migration into production • Moving the right components • Enabling Rollback & Recovery Agility dictates frequent changes & new tools are needed to streamline the process Development Operations
- 53. What is DBmaestro TeamWork • Database Enforced Change Management + Database version control + Plugs into the ALM (change request, tickets & work items) + Database change impact analysis + Database deployment automation • DevOps Solution for databases + Deployment, rollback & recovery + Plugs into release management
- 54. The Challenges that DBmaestro Addresses • • • • Development delays Silos in development, DBA and operations Delays in deployment (internally and to operations) Errors in production
- 55. Development Delays • Different methodologies for the application & database • Code overrides • Lack of history of changes (who did what, where, when and why) • Manual writing of delta scripts • Lack of automation
- 56. Silos in Development, DBA and Operations • No sharing between the team • No visibility • Always looking for errors made by others
- 57. Delays in Deployment (Internally and to Operations) • Deployment automation does not really include the database tier • Database scripts generated out of the scope of automation • Lack of confidence in automation
- 58. Errors in Production • Missing changes • Deploying the wrong version of objects • What about the reference data?
- 59. Poll Which challenges have you experienced? 1. Development delays 2. Silos in development, DBA and operations 3. Delays in deployment (internally and to operations) 4. Errors in production
- 60. How? • Database version control – – – – Enforced Check Out/In Labels Rollback/Undo Audit trail reports • Database impact analysis – Utilizes version control repository information – 3 way analysis • Database deployment automation – – – – API Baselines Conflict resolution Customized business logic
- 61. Without DCM - Two isolated Processes Version Control Process Development Process Check-Out Script ? Check-In Script ? ? Modify Script Get updated Script from DB ? Compile Script in DB Debug Script in DB
- 62. With DCM - One Enforced Process Development & Version Control Process Check-Out Object Check-In Object Modify Object in DB Run Applications’ Tests
- 63. Safety Net For Automation of Deployment Simple Compare & Sync Source vs. Target = ≠ Action No Action ? You do not have all of the information Baseline aware Deployment Source vs. Baseline Target vs. Baseline Action = = No Action ≠ = Override = ≠ Ignore ≠ ≠ Merge With Baselines and 3 way analysis the unknown is now known
- 64. Benefits - Development • Database change repository • Follow SCM best practices (Check-Out/CheckIn) • All changes are documented • Manage who can do what, where, when & why
- 65. Benefits - Operations • Integrated deployment engine • Business level audit • Roles & responsibilities enforcement
- 66. Benefits - Management • Complete visibility into changes in progress • Management reports • No silos
- 67. Live Demo • Clone 2 virtual copies of the Trunk 1. Dev1 2. Dev2 • Make changes & merge them into the Trunk: Developer1 modifies Dev1 Developer1 merges changes into the Trunk Developer2 modifies Dev2 Developer2 merges changes into the Trunk • Rely on enforced changes & automation
- 68. Developer 1 modify Dev1 Instance Virtual Database DB VC Dev2 Instance Virtual Database Developer 2 modify Trunk Instance Time Window Virtual Database
- 71. Q&A Kyle Hailey @kylehhailey Delphix: delphix.com Uri Margalit @UriMargalit DBmaestro: dbmaestro.com
- 72. Thanks
Notes de l'éditeur
- Founded in 2008, launched in 2010Jedidiah Yueh, President and CEOFounded Avamar in 1999, sold to EMC in 2006, VP Product Mgmt at EMCAvamar: >$1B revenue, 150 Employees: HQ in Menlo Park, SF, Boston, DC, London, NY and AtlantaGrowing 250% annually – 130+ customers including 100 Fortune1000 Customers
- Founded in 2008Part of the Extreme Group which has about 180 IT professionals consultants
- The topic of today webinar is how you can have your database development under version control principles and methods to catch up with your application development and how you can utilize the database virtualization in order to clone your database for parallel development
- From the business point of view, they would like to have their system uptodate with no downtime.However researches show that:* 80% of outages, impact mission critical services* From the 80%, 50% are due to human errors* And In addition 40% of changes fail and require rollbackThis sums into many hours in which the systems are down and of course reflect the business.80%of outages impacting mission-critical services caused by people and process issues thru 2015, with the majority of those outages (50%+) caused by change/configuration/release integration and hand-off issues (Gartner RAS Core Research Note G00208328 Ronni J. Colville, George Spafford [October 27, 2010] – Strategic Planning Assumption(s) “Top Seven Considerations for Configuration Management for Virtual and Cloud )
- From the technical point of view, Developers, DBA and basically no one like to repeat tasks over and over again.And we would like to have a system that will remember the changes we made 6 months ago and to allow us to focus on development tasks and not on overhead tasks and definitely not to repeat ourselves – this is why we want automation
- In software development you cannot talk on automation and not to mention Agile & DevOps.So how we deal with the risk of changes, we release smaller and more focused changes while we believe they are easier to managed.We do it often, so we must have some automation (because a. we don’t want to repeat ourselves and b. we don’t want errors in this critical process)This is called Agile, but so far we were doing staff internally.With DevOps we can release the laser-focus changes to the operations often with confident.
- No one today think on developing software without having any version control solution that will manage the changes, keep track on the history.But when considering the version control, a small but critical tier in the application is left aside – the database
- Database is also part of the application.Code can be in several ways, schema structure, business code written in PL/SQL and reference data in lookup tables.The main reasons why database code is not native to traditional file based version control are that database is a central resource, objects within the database cannot being dropped and created as done to source code and as done in deployment to the executables.This brings us to a big challenge, how can we branch a database or clone it easily in order to be more agile? With this in mind, I’ll let Kyle answer.
- You might be familiar with this cycle that we’ve seen in the industry:Where IT departments budgets are being constrainedWhen IT budgets are constrained one of the first targets is reducing storageAs storage budgets are reduced the ability to provision database copies and development environments goes downAs development environments become constrained, projects start to hit delays. As projects are delayed The applications that the business depend on to generate revenue to pay for IT budgets are delayedWhich reduces revenue as the business cannot access new applications Which in turn puts more pressure on the IT budget.It becomes a viscous circle
- There is saying in the industry that we want “good, cheap, fast: choose two”Meaning we want to build applications quickly, ie fast, we want those applications to have good functionality and we want those applications to be \cheap to buildBut we can’t have all three.
- I don’t knowIf these situations ring a bell at your organization orif you can imagine some of these situations But here are some of the issues we at Delphix are seeing in the industry with the companies we are talking to.Let’s look at the 5 points in more detail
- We talked to Presbyterian HealthcareAnd they told us that they spend 96% of their QA cycle time building the QA environmentAnd only 4% actually running the QA suiteThis happens for every QA suitemeaningFor every dollar spent on QA there was only 4 cents of actual QA value Meaning 96% cost is spent infrastructure time and overhead
- Because of the time required to set up QA environmentsThe actual QA tests suites lag behind the end of a sprint or code freezeMeaning that the amount of time that goes by after the introduction of a bug in code and before the bug is found increasesAnd the more time that goes by after the introduction of a bug into the codeThe more dependent is written on top of the bug Increasing the amount of code rework required after the bug is finally foundIn his seminal book that some of you may be familiar with, “Software Engineering Economics”, author Barry Boehm Introduce the computer world to the idea that the longer one delays fixing a bug in the application design lifescyleThe more expensive it is to to fix that bug and these cost rise exponentially the laterThe bug is address in the cycle
- Not sure if you’ve run into this but I have personally experience the followingWhen I was talking to one group at Ebay, in that development group they Shared a single copy of the production database between the developers on that team.What this sharing of a single copy of production meant, is that whenever a Developer wanted to modified that database, they had to submit their changes to codeReview and that code review took 1 to 2 weeks.I don’t know about you, but that kind of delay would stifle my motivationAnd I have direct experience with the kind of disgruntlement it can cause.When I was last a DBA, all schema changes went through me.It took me about half a day to process schema changes. That delay was too much so it was unilaterally decided byThey developers to go to an EAV schema. Or entity attribute value schemaWhich mean that developers could add new fields without consulting me and without stepping on each others feat.It also mean that SQL code as unreadable and performance was atrocious.Besides creating developer frustration, sharing a database also makes refreshing the data difficult as it takes a while to refresh the full copyAnd it takes even longer to coordinate a time when everyone stops using the copy to make the refreshAll this means is that the copy rarely gets refreshed and the data gets old and unreliable
- To circumvent the problems of sharing a single copy of productionMany shops we talk to create subsets.One company we talked to , RBS spends 50% of time copying databases have to subset because not enough storagesubsetting process constantly needs fixing modificationNow What happens when developers use subsets -- ****** -----
- Stubhub (ebay) estimates that 20% of there production bugs arise from testing onSubsets instead of full database copies.
- The biggest and most pervasive problem we see is slow build times.In order to set up an database copy for a development environmentsRequires submitting a request to management who has to review itThen if the request is granted, it is passed to the DBA who has to coordinate with the Sysadmin who has to coordinate with the storage admin.In such a situation it makes sense that copying a large database would take a long timeBut even when we talk to someone who uses netapp storage snapshots like Electronics Art, they said even using storage snapshot sit took2-4 days to get a database clone copy due to the coordination between DBA, sys admin and storage adminAt many of the customers we talk to provisioning a database clone copy takes weeks or months.One large global bank quotes us as taking typically 6 months to provision a database clone copy environment.Requirements: self service for app teamsRequirements: end-to-end automationMetrics: # people, process, time for deliverySo far we have talked about the weight of infrastructure on app delivery. Of course, to control and manage that infrastructure, firms layer on a large set of bureaucratic processes, change control, approvals, procurement, governance, etc etc. So the operational and organizational hurdles then create an even bigger drag on IT and app development.Here’s an example from one banking customer.Once the app developer puts in a request for a new development environment, there’s at least a week long wait for management approvals. Then project DBA work with the sysadmin and storage groups for capacity. If more capacity needs to be allocated, it’s 3 more days. If more needs to be purchased, weeks or months. If a copy of production data is needed, the process needs to wait on a production DBA, who might be busy with production issues. Recovering the database to a specific point in time and configuration can also take days.It is very common for two weeks to pass between a developer request and a ready environment. The process can be repeated for multiple environments, for data refreshes, and for integration across multiple systems.With Delphix, turns stop signs into green lights. Provisioning, refresh, rollback, and data integration happen nearly instantly and do not trigger approvals from production systems or require additional storage. That is why KLA is able to deliver 5 times the output from its SAP teams…Without Delphix, it’s impossible for organizations to implement the level of agile processes they desire. The management of data, and the bureaucracy of data management, slows things down too much.
- Due to the constraints of building clone copy database environments one ends up in the “culture of no”Where developers stop asking for a copy of a production database because the answer is “no”If the developers need to debug an anomaly seen on production or if they need to write a custom module which requires a copy of production they know not to even ask and just give up.
- The problem is getting the right data to the right people at the right time
- As Vmware takes a single set of hardware and provisions many virtual machinesDelphix takes a set of datafiles and provisions many virtual database clones
- In the physical database world, 3 clones take up 3x the storage.In the virtual world 3 clones take up 1/3 the storage thanks to block sharing and compression
- Software installs an any x86 hardware uses any storage supports Oracle 9.2-12c, standard edition, enterprise edition, single instance and RAC on AIX, Sparc, HPUX, LINUX support SQL Server
- EMC,Netapp, Fujitsu, Or newer flash storage likeViolin, Pure Storage, Fusion IO etc
- Delphix does a one time only copy of the source database onto Delphix
- Quote from a customer “Delphix GUI is what Oracle Enterprise Manager would look like if Apple had designed it”Delphix inter face is user friendly, polished and easy to use
- Source Syncing* Initial backup once onlyContinual forever change collection Purging of old data Storage DxFSShare blocks snap shots , unlimited, storage agnosticCompression , 1/3 typically, compress on block boundaries. Overhead for compression is basically undetectable Share data in memory, super caching*Self Service AutomationVirtual database provisioning, rollback, refresh*, branching*, tagging*Mount files over NFSInit.ora, SID, database name, database unique nameSecurity on who can see which source databases, how many clones they can make and how much storage they can use
- Presbyterian when from 10 hour builds to 10 minute buildsTotal Investment in Test Environment: $2M/year10 QA engineersDBA, storage team dedicated to support testingApp, Oracle server, storage, backupsRestore load competes with backup jobsRequirements: fast data refresh, rollbackData delivery takes 480 out of 500 minute test cycle (4% value)$.04/$1.00 actual testing vs. setup
- For example Stubhub went from 5 copies of production in development to 120Giving each developer their own copy
- Stubhub estimated a 20% reduction in bugs that made it to production
- ExamlplesMacys 4000 hours/year cloning to 8 hours/yearKLA-Tencor over doubled project output, like taking 100 person team and making it a 200 person teamHealth Dialogue reduced storage from 720TB tos 8TB
- Thanks Kyle, I'm sure that everyone will agree the opportunities with Delphix are very excited, the idea of spin up the databases very quickly it is exciting.
- DBmaestro TeamWork is a solutions that enables you to take control your database development from the very beginning using version control.It is integrated with the existing ALM tools you already have.All changes are stored in a version control repository which is connected to the impact analysis module. More than just compare & sync.With automation in mind, you can complete your DevOps or Continuous Delivery or Continuous Deployment processes and if there is a slight change that something should not be promoted, TeamWork will make sure you know about it.
- The challenges we’ve seen when we speak with people that there are:Many delay in developmentMany silos in the organization and within the development teamMany organizations still miss the due date of releasesThere are errors in productions