In this webinar Kyle Kingsbury will talk about the testing process, how Jepsen works and what he found in VoltDB. Then listen to John Hugg, the Founding Engineer at VoltDB to learn why these tests are important, how VoltDB reacted to test findings and the QA process moving forward.

1. Kyle Kingsbury Talks about the
Jepsen Test: !
!
What VoltDB Learned About
Data Accuracy and Consistency!
or!
presents…!

2. Presenters!
John Hugg, VoltDB Inc.
Founding Engineer at VoltDB
Was involved with both adding bugs
found by Jepsen and fixing them.
Kyle Kingsbury, Jepsen.io (aphyr)
Creator of Jepsen, Riemann, Tesser
Likes to break databases.
Broke VoltDB, in fact.

3. Why did VoltDB
pick Jepsen?
What is VoltDB and
what’s it good for?
Learn More
So What?

4. Jepsen
Central
voltdb.com/jepsen

5. voltdb.com/jepsen
jepsen.io pages
VoltDB’s Jepsen-specific
content
Learn more about VoltDB
Hacker News comments

6. What is VoltDB?
• Scale-out, clustered, SQL Relational database
• Strong serializable transactions by default, even
at high scale
• An excellent processing engine with rich import/
export functionality

7. Use Cases
• Anything where decisions based on logic are made
in real time for incoming events:
• Policy Enforcement
• Fraud Detection
• Ad Tech
• Real-time personalization
• Payments
• Anything where math / calculations are done:
• Billing and reporting on live data
• State tracking

8. Example: Telco
Mobile phone is
dialed.
Request sent to
VoltDB to decide if it
should be let
through.
Single transaction looks at state
and decides if this call:
is fraudulent?
is permitted under plan?
has prepaid balance to cover?
State
Blacklists
Fraud Rules
Billing Info
Recent Activity for both Numbers
Export
to OLAP

9. Example: Micro
Personalization
User clicks link on
a website. This
generates a
request to VoltDB.
VoltDB transaction
scans a table of rules
and checks which
apply to this event.
Eventually the
transaction decides
what to show the user
next.
That decision is
exported to HDFS
Spark ML is used to look at
historical data in HDFS and
generate new rules.
These rules are loaded into
VoltDB every few hours.
User sees
personalized
content

10. StateProcessing

11. Complex,
Transactional
Business Logic
Scale-Out
Performance
Streaming Events
SQL Relational State

12. Why Jepsen?
• We are always hungry for tests!
• Could build VoltDB-Jepsen harness
ourselves, but…
Wouldn’t be as good and wouldn’t have
Kingsbury’s credibility.
• Customers have asked about it.
• Kingsbury has a built-in audience (marketing)

60. VoltDB’s Takeaway

61. VoltDB’s Takeaway
Serious question:
What’s the worst that
could happen?

62. VoltDB Takeaway
• Our policy: Consistency or data loss bugs are
blocking bugs to be prioritized above all else.
• So if Jepsen finds bugs, we need to fix them
ASAP.
• The risk is that Jepsen finds bugs that we have to
fix, which might impact our schedule.
• But that’s dumb. If our product has bugs, not
knowing about them doesn’t make them not there.

63. VoltDB Takeaway
Marketing & Perception:
• Passing Jepsen is good. People talking about
VoltDB is good. Showing we care about this
stuff is good.
• Having bugs is bad, but discussing and fixing
issues openly and seriously can be positive.

65. Reproducible!
• 100% reproducible test:
• Set up Jepsen from
Github
• Clone Jepen VoltDB
driver from Github
• Run!
• Can’t do this with systems
you don’t control.
github.com/jepsen-io/voltdb!

66. I want to learn more!
chat.voltdb.com
forum.voltdb.com
askanengineer
@voltdb.com
@johnhugg
@voltdb
@aphyr
voltdb.com/jepsen