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Fighting Insanity
Will Bleker
Gary Stewart
Amsterdam, 14 February 2019
2
About us
3
EMPLOYED_BY
Role: Platform Architect
Gary Stewart
Will Bleker
Concepts
4
• Concepts
• Use case
• Learnings from our use-case
A little history …
5
Pets
• Unique and indispensable
• Often long lived
• Scale-up
• Active-passive architecture, pairs of servers, etc
Cattle
• Disposable, one of a herd
• Often short lived
• Scale-out
• Active-active architecture
• ‘Cattle’ datastores include
• Apache Cassandra
• Apache Kafka
Databases are much like the transport industry
• Undergone massive transformations
• Various types suitable for different needs
• Trains
• Relatively expensive
• Built on-demand
• Longer lifespan
• Cars
• Manual to mass production
• Almost consumable
• Often cheaper to rebuild, recycle than repair
Our twist – trains versus cars
6
Our journey to NoSQL
7
A few years back we started with Cassandra
• Exotic key/value store (column family)
• Distributed active-active architecture
• Tuneable consistency
• Paradigm shift
• Nights and weekends are now free (including LCM)
• However now a liquid expectation!
RDBMSNoSQL
Adding graph to the landscape
8
Gap in our DB landscape - connected data
• Another NoSQL database!
Interesting observation
• Key/Value easy to mess up a partition
• RDBMS easy to mess up a table
• Graph easy to mess up the database!
?
RDBMSNoSQL Graph
Typical architecture for caching data
9
SOR
Challenges include
• Initial load is usually once-off or manual
• Lifecycle management (LCM) is hard and per component
• Resilience patterns need to be applied everywhere
• Cross DC resilience pattern also required
Real-Time
Sync
Batch
Loader
Cache
Cache
Cache ‘Cattle’ Paradigm
10
SOR
Real-Time
Sync
Single
Materialized ViewCommit Log
Challenges include
• Initial load Integrated by design
• Lifecycle management (LCM) Simplified
• Resilience Simplified
• Cross DC Simplified
Batch
Loader
Cache ‘Cattle’ Paradigm – Taking it further
11
SOR
Real-Time
Sync
Single
Materialized ViewCommit Log
E.g. Apache Kafka supports compacting topics
• Ideal for rebuilding cache use cases
Batch
Loader
Cache ‘Cattle’ Paradigm – Going all the way
12
Real-Time
Sync
Single
Materialized ViewCommit Log
Batch
Loader
SOR
Tip: More details on Pipeline and admin-import utility
http://goo.gl/ZY7NES
Use case
13
• Concepts
• Use case
• Learnings from our use-case
Cassandra Platform Team
14
Cassandra Platform Team
15
Spiralling out of control
16
• Victim of our own success
• Too many data-models to review
• Multi-tenancy (extreme)
• Configuration drift
• Way too many metrics
• Repeatable mistakes
• Hero culture
• Quick tools that needed person X
• Morale was taking its toll
17
Doing the same thing
over and over again and
expecting different results
- Albert Einstein
INSANITY !!!
• Commit to HARD agreements
• Configuration items
• Ownership
• NO budging
• Track it
• Do the tedious work
• Feel the pain
Breaking insanity
18
• Focus on the problem at hand
• Stay away from ‘new’ stuff not working
• Stick to agreements
• Pipeline everything
• We don’t value initial bursts of energy
• Pipeline the heroes 
• Reap benefits of the Graph PoC
• Introduce max 1 ‘unknown’ item at a time
Setting the priorities
19
Architecture
20
• Simple and effective
• Executes ‘Ops’ commands:
• ps –ef
• netstat
• cat config.json
• Raw output sent as message
• All parsing rules for raw messages
• Store in databases
• Complex rules and analysis
Architecture
Commit LogProducer Materialized view
Consumer
App
21
Consuming message (data pipeline)
• Parse
• Store as Key/Value
• Calculate
• Observe
• Recommend
Principles
• Compare to inventory not ‘self discovery’
• Define agreements in code
• No data migrations allowed (simply rebuild!)
Consumer - Graph Model
22
Learnings
from our use-case
23
• Concepts
• Use case
• Learnings from our use-case
Accidentally used same labels
• E.g. Source != Source
• Reads become trickier
• Confuses meta-data
• CALL db.schema()
Solutions
• Property existence feature
• Solve your reads with writes!
• Document graph
• Remove duplicate names
Modify code
 Simply rebuild and …
Pay attention to the model
24
25
Simple model
• (:Source)-[:HAS_MSG]->(:Message)
Lots of nodes
26
Consider traversing to reduce density
• (:Source)-[:LAST]->(:Message)->[:PREV]->(:Message)
Modify code
 Simply rebuild and 
Partitioning in graph
27
28
MATCH …
OPTIONAL MATCH
WHERE …
MERGE
MATCH …
WHERE …
OPTIONAL MATCH
MERGE
 Simply rebuild and 
Attempted to migrate data anyway
29
30
Connected data
31
Commit LogProducer Materialized view
Consumer
App
32
Connected data – using indirection
33
Connected data
34
Keyspace Client Expected DC Expected Num
Connected Hosts
Found Connection
From DC
Found Num
Connected Hosts
Observation
Keyspace1 Client1 DC1:DC2 5:5 DC1 3:0 Missing DC
Not fully connected.
Keyspace1 Client2 DC1:DC2 5:5 DC1:DC2 3:3 Not fully connected.
Keyspace2 Client3 DC1:DC2 5:5 DC1:DC2 3:5 Not fully connected.
Keyspace2 Client4 DC1:DC2 5:5 DC1:DC2 3:5 Not fully connected.
…
Connected data
35
Graph proven to be a VERY good fit for this use case!
• Already producing 50+ commands
• Heroes have a place to ‘query’
• Various unexpected findings
• Misconfigured tenants
• 100+ recommendations made
Architecture now helps with
• Allow us to be ‘in’ control without ‘having’ control
• Facilitates being agile
• Pipelining the data
• Learning new technologies
300+ cars were destroyed in our short journey to save our sanity!
So what happened?
36
Conclusions
37
Know your graph model
Don’t forget to ’partition’ in graph
Graphs help with indirection ‘views’
Design for rebuild
Short summary
38
Don’t forget to fight insanity …
39
Doing the same thing
over and over again and
expecting different results
- Albert Einstein
Thank you
Wilhelmus.Bleker@ing.com - @WBleker
Gary.Stewart@ing.com - @Gaz_GandA
Fighting Insanity
40
Follow us to stay a step ahead
ING.com
YouTube.com/ING
SlideShare.net/ING@ING_News LinkedIn.com/company/ING
Flickr.com/INGGroupFacebook.com/ING
ING Group’s Annual Accounts are prepared in accordance with
International Financial Reporting Standards as adopted by the
European Union (‘IFRS-EU’).
In preparing the financial information in this document, the same
accounting principles are applied as in the 2014 ING Group Annual
Accounts. All figures in this document are unaudited. Small
differences are possible in the tables due to rounding.
Certain of the statements contained herein are not historical facts,
including, without limitation, certain statements made of future
expectations and other forward-looking statements that are based on
management’s current views and assumptions and involve known
and unknown risks and uncertainties that could cause actual results,
performance or events to differ materially from those expressed or
implied in such statements. Actual results, performance or events
may differ materially from those in such statements due to, without
limitation: (1) changes in general economic conditions, in particular
economic conditions in ING’s core markets, (2) changes in
performance of financial markets, including developing markets, (3)
consequences of a potential (partial) break-up of the euro, (4) the
implementation of ING’s restructuring plan to separate banking and
insurance operations, (5) changes in the availability of, and costs
associated with, sources of liquidity such as interbank funding, as
well as conditions in the credit markets generally, including changes
in borrower and counterparty creditworthiness, (6) the frequency and
severity of insured loss events, (7) changes affecting mortality and
morbidity levels and trends,(8) changes affecting persistency levels,
(9) changes affecting interest rate levels, (10) changes affecting
currency exchange rates, (11) changes in investor, customer and
policyholder behaviour, (12) changes in general competitive factors,
(13) changes in laws and regulations, (14) changes in the policies of
governments and/or regulatory authorities, (15) conclusions with
regard to purchase accounting assumptions and methodologies, (16)
changes in ownership that could affect the future availability to us of
net operating loss, net capital and built-in loss carry forwards, (17)
changes in credit ratings, (18) ING’s ability to achieve projected
operational synergies and (19) the other risks and uncertainties
detailed in the Risk Factors section contained in the most recent
annual report of ING Groep N.V. Any forward-looking statements
made by or on behalf of ING speak only as of the date they are
made, and, ING assumes no obligation to publicly update or revise
any forward-looking statements, whether as a result of new
information or for any other reason.
This document does not constitute an offer to sell, or a solicitation of
an offer to purchase, any securities in the United States or any other
jurisdiction. The securities of NN Group have not been and will not
be registered under the U.S. Securities Act of 1933, as amended
(the “Securities Act”), and may not be offered or sold within the
United States absent registration or an applicable exemption from
the registration requirements of the Securities Act.
www.ing.com
Disclaimer
42

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GraphTour - ING - Fighting insanity

  • 1. Fighting Insanity Will Bleker Gary Stewart Amsterdam, 14 February 2019
  • 2. 2
  • 3. About us 3 EMPLOYED_BY Role: Platform Architect Gary Stewart Will Bleker
  • 4. Concepts 4 • Concepts • Use case • Learnings from our use-case
  • 5. A little history … 5 Pets • Unique and indispensable • Often long lived • Scale-up • Active-passive architecture, pairs of servers, etc Cattle • Disposable, one of a herd • Often short lived • Scale-out • Active-active architecture • ‘Cattle’ datastores include • Apache Cassandra • Apache Kafka
  • 6. Databases are much like the transport industry • Undergone massive transformations • Various types suitable for different needs • Trains • Relatively expensive • Built on-demand • Longer lifespan • Cars • Manual to mass production • Almost consumable • Often cheaper to rebuild, recycle than repair Our twist – trains versus cars 6
  • 7. Our journey to NoSQL 7 A few years back we started with Cassandra • Exotic key/value store (column family) • Distributed active-active architecture • Tuneable consistency • Paradigm shift • Nights and weekends are now free (including LCM) • However now a liquid expectation! RDBMSNoSQL
  • 8. Adding graph to the landscape 8 Gap in our DB landscape - connected data • Another NoSQL database! Interesting observation • Key/Value easy to mess up a partition • RDBMS easy to mess up a table • Graph easy to mess up the database! ? RDBMSNoSQL Graph
  • 9. Typical architecture for caching data 9 SOR Challenges include • Initial load is usually once-off or manual • Lifecycle management (LCM) is hard and per component • Resilience patterns need to be applied everywhere • Cross DC resilience pattern also required Real-Time Sync Batch Loader Cache Cache
  • 10. Cache ‘Cattle’ Paradigm 10 SOR Real-Time Sync Single Materialized ViewCommit Log Challenges include • Initial load Integrated by design • Lifecycle management (LCM) Simplified • Resilience Simplified • Cross DC Simplified Batch Loader
  • 11. Cache ‘Cattle’ Paradigm – Taking it further 11 SOR Real-Time Sync Single Materialized ViewCommit Log E.g. Apache Kafka supports compacting topics • Ideal for rebuilding cache use cases Batch Loader
  • 12. Cache ‘Cattle’ Paradigm – Going all the way 12 Real-Time Sync Single Materialized ViewCommit Log Batch Loader SOR Tip: More details on Pipeline and admin-import utility http://goo.gl/ZY7NES
  • 13. Use case 13 • Concepts • Use case • Learnings from our use-case
  • 16. Spiralling out of control 16 • Victim of our own success • Too many data-models to review • Multi-tenancy (extreme) • Configuration drift • Way too many metrics • Repeatable mistakes • Hero culture • Quick tools that needed person X • Morale was taking its toll
  • 17. 17 Doing the same thing over and over again and expecting different results - Albert Einstein INSANITY !!!
  • 18. • Commit to HARD agreements • Configuration items • Ownership • NO budging • Track it • Do the tedious work • Feel the pain Breaking insanity 18
  • 19. • Focus on the problem at hand • Stay away from ‘new’ stuff not working • Stick to agreements • Pipeline everything • We don’t value initial bursts of energy • Pipeline the heroes  • Reap benefits of the Graph PoC • Introduce max 1 ‘unknown’ item at a time Setting the priorities 19
  • 20. Architecture 20 • Simple and effective • Executes ‘Ops’ commands: • ps –ef • netstat • cat config.json • Raw output sent as message • All parsing rules for raw messages • Store in databases • Complex rules and analysis
  • 22. Consuming message (data pipeline) • Parse • Store as Key/Value • Calculate • Observe • Recommend Principles • Compare to inventory not ‘self discovery’ • Define agreements in code • No data migrations allowed (simply rebuild!) Consumer - Graph Model 22
  • 23. Learnings from our use-case 23 • Concepts • Use case • Learnings from our use-case
  • 24. Accidentally used same labels • E.g. Source != Source • Reads become trickier • Confuses meta-data • CALL db.schema() Solutions • Property existence feature • Solve your reads with writes! • Document graph • Remove duplicate names Modify code  Simply rebuild and … Pay attention to the model 24
  • 25. 25
  • 27. Consider traversing to reduce density • (:Source)-[:LAST]->(:Message)->[:PREV]->(:Message) Modify code  Simply rebuild and  Partitioning in graph 27
  • 28. 28
  • 29. MATCH … OPTIONAL MATCH WHERE … MERGE MATCH … WHERE … OPTIONAL MATCH MERGE  Simply rebuild and  Attempted to migrate data anyway 29
  • 30. 30
  • 32. Commit LogProducer Materialized view Consumer App 32
  • 33. Connected data – using indirection 33
  • 34. Connected data 34 Keyspace Client Expected DC Expected Num Connected Hosts Found Connection From DC Found Num Connected Hosts Observation Keyspace1 Client1 DC1:DC2 5:5 DC1 3:0 Missing DC Not fully connected. Keyspace1 Client2 DC1:DC2 5:5 DC1:DC2 3:3 Not fully connected. Keyspace2 Client3 DC1:DC2 5:5 DC1:DC2 3:5 Not fully connected. Keyspace2 Client4 DC1:DC2 5:5 DC1:DC2 3:5 Not fully connected. …
  • 36. Graph proven to be a VERY good fit for this use case! • Already producing 50+ commands • Heroes have a place to ‘query’ • Various unexpected findings • Misconfigured tenants • 100+ recommendations made Architecture now helps with • Allow us to be ‘in’ control without ‘having’ control • Facilitates being agile • Pipelining the data • Learning new technologies 300+ cars were destroyed in our short journey to save our sanity! So what happened? 36
  • 38. Know your graph model Don’t forget to ’partition’ in graph Graphs help with indirection ‘views’ Design for rebuild Short summary 38
  • 39. Don’t forget to fight insanity … 39 Doing the same thing over and over again and expecting different results - Albert Einstein
  • 40. Thank you Wilhelmus.Bleker@ing.com - @WBleker Gary.Stewart@ing.com - @Gaz_GandA Fighting Insanity 40
  • 41. Follow us to stay a step ahead ING.com YouTube.com/ING SlideShare.net/ING@ING_News LinkedIn.com/company/ING Flickr.com/INGGroupFacebook.com/ING
  • 42. ING Group’s Annual Accounts are prepared in accordance with International Financial Reporting Standards as adopted by the European Union (‘IFRS-EU’). In preparing the financial information in this document, the same accounting principles are applied as in the 2014 ING Group Annual Accounts. All figures in this document are unaudited. Small differences are possible in the tables due to rounding. Certain of the statements contained herein are not historical facts, including, without limitation, certain statements made of future expectations and other forward-looking statements that are based on management’s current views and assumptions and involve known and unknown risks and uncertainties that could cause actual results, performance or events to differ materially from those expressed or implied in such statements. Actual results, performance or events may differ materially from those in such statements due to, without limitation: (1) changes in general economic conditions, in particular economic conditions in ING’s core markets, (2) changes in performance of financial markets, including developing markets, (3) consequences of a potential (partial) break-up of the euro, (4) the implementation of ING’s restructuring plan to separate banking and insurance operations, (5) changes in the availability of, and costs associated with, sources of liquidity such as interbank funding, as well as conditions in the credit markets generally, including changes in borrower and counterparty creditworthiness, (6) the frequency and severity of insured loss events, (7) changes affecting mortality and morbidity levels and trends,(8) changes affecting persistency levels, (9) changes affecting interest rate levels, (10) changes affecting currency exchange rates, (11) changes in investor, customer and policyholder behaviour, (12) changes in general competitive factors, (13) changes in laws and regulations, (14) changes in the policies of governments and/or regulatory authorities, (15) conclusions with regard to purchase accounting assumptions and methodologies, (16) changes in ownership that could affect the future availability to us of net operating loss, net capital and built-in loss carry forwards, (17) changes in credit ratings, (18) ING’s ability to achieve projected operational synergies and (19) the other risks and uncertainties detailed in the Risk Factors section contained in the most recent annual report of ING Groep N.V. Any forward-looking statements made by or on behalf of ING speak only as of the date they are made, and, ING assumes no obligation to publicly update or revise any forward-looking statements, whether as a result of new information or for any other reason. This document does not constitute an offer to sell, or a solicitation of an offer to purchase, any securities in the United States or any other jurisdiction. The securities of NN Group have not been and will not be registered under the U.S. Securities Act of 1933, as amended (the “Securities Act”), and may not be offered or sold within the United States absent registration or an applicable exemption from the registration requirements of the Securities Act. www.ing.com Disclaimer 42

Editor's Notes

  1. Gary
  2. Gary
  3. Gary  Will
  4. Will
  5. Will
  6. Will
  7. Will
  8. Will  Gary
  9. Gary
  10. Gary
  11. Gary
  12. Gary  Will
  13. Will
  14. Will
  15. Will
  16. Will
  17. Gary
  18. Gary
  19. Gary -> Will
  20. Will
  21. Will  Gary
  22. Gary
  23. Gary
  24. Gary Blurring due to irrelevance
  25. Gary
  26. Gary
  27. Gary
  28. Gary
  29. Gary
  30. Gary
  31. Gary
  32. Gary
  33. Gary
  34. Gary
  35. Gary -> Will
  36. Will
  37. Will
  38. Will
  39. Will