1.
Engineering with Open Source
B U I L D I N G A H I G H P E R F O R M A N C E M E T R I C S S Y S T E M
U S I N G O P E N S O U R C E S O F T W A R E
#GHC18
Hyonjee Joo | @twosigma

2.
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PRESENTED BY ANITAB.ORG AND THE ASSOCIATION FOR COMPUTING MACHINERY
This document is being distributed for informational and
educational purposes only and is not an offer to sell or the
solicitation of an offer to buy any securities or other
instruments. The information contained herein is not intended
to provide, and should not be relied upon for, investment
advice. The views expressed herein are not necessarily the
views of Two Sigma Investments, LP or any of its affiliates
(collectively, “Two Sigma”). Such views reflect the
assumptions of the author(s) of the document and are subject
to change without notice. The document may employ data
derived from third-party sources. No representation is made by
Two Sigma as to the accuracy of such information and the use
of such information in no way implies an endorsement of the
source of such information or its validity.
The copyrights and/or trademarks in some of the images, logos
or other material used herein may be owned by entities other
than Two Sigma. If so, such copyrights and/or trademarks are
most likely owned by the entity that created the material and
are used purely for identification and comment as fair use
under international copyright and/or trademark laws. Use of
such image, copyright or trademark does not imply any
association with such organization (or endorsement of such
organization) by Two Sigma, nor vice versa.
Legal
Disclaimer
#GHC 18

3.
Introduction
#GHC18

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My Background
#GHC18
• Graduated from Columbia University
• B.S. in computer science and psychology
• My 4th GHC – 1st time participating in OSD!
• Currently, a software engineer at Two Sigma in New York
• What is Two Sigma?
• Investment management firm that uses technology and lots of data to drive
decisions

5.
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• Walk through designing a metrics system for a high performance data
platform
5
problem
solution
problem
problem
solution solution
goal
#GHC18
In this talk…

6.
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• Walk through designing a metrics system for a high performance data
platform
• Using open source solutions every step of the way
6
problem
solution
problem
problem
solution solution
goal
#GHC18
In this talk…

7.
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1. Engineering a new system can involve less code than you think
7
#GHC18
The Takeaways

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1. Engineering a new system can involve less code than you think
2. Know the problem before you look for a solution
#GHC18
The Takeaways

9.
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1. Engineering a new system can involve less code than you think
2. Know the problem before you look for a solution
3. Careful what you choose, not all open source tools are made (or
supported) equally
9
#GHC18
The Takeaways

10.
Let’s set up the problem
#GHC18

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• We want to measure usage metrics because:
• It’s important to know how our system is being used and by who
• If we know what people want to do, we can do a better job doing it
• We can identify trends and anticipate how user needs may change
11
#GHC18
Purpose of a Metrics System

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#GHC18
The Data Platform
High performance data
platform
Up to 50,000 queries/sec
1.85 GiB/sec per node
Example query:
data = client.query(
date_range=(20000101, 20180101),
dataset=”x”,
transformation=”log”)

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#GHC18
Query Data
Query 1
{
query_time: “20180209 09:00:05”,
user: “user1”,
dataset: “x”,
date_range: {
begin: “20000101”,
end: “20180101”
},
duration: 100,
bytes: 350000000,
query_param_1: 1.0,
query_param_2: “log”,
...
}

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#GHC18
Query Data
Query 1
{
query_time: “20180209 09:00:05”,
user: “user1”,
dataset: “x”,
date_range: {
begin: “20000101”,
end: “20180101”
},
duration: 100,
bytes: 350000000,
query_param_1: 1.0,
query_param_2: “log”,
...
}

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#GHC18
Query Data
Query 1
{
query_time: “20180209 09:00:05”,
user: “user1”,
dataset: “x”,
date_range: {
begin: “20000101”,
end: “20180101”
},
duration: 100,
bytes: 350000000,
query_param_1: 1.0,
query_param_2: “log”,
...
}

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#GHC18
Query Data
Query 1
{
query_time: “20180209 09:00:05”,
user: “user1”,
dataset: “x”,
date_range: {
begin: “20000101”,
end: “20180101”
},
duration: 100,
bytes: 350000000,
query_param_1: 1.0,
query_param_2: “log”,
...
}

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#GHC18
Query Data
Query 1
{
query_time: “20180209 09:00:05”,
user: “user1”,
dataset: “x”,
date_range: {
begin: “20000101”,
end: “20180101”
},
duration: 100,
bytes: 350000000,
query_param_1: 1.0,
query_param_2: “log”,
...
}

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#GHC18
Query Data
Query 1
{
query_time: “20180209 09:00:05”,
user: “user1”,
dataset: “x”,
date_range: {
begin: “20000101”,
end: “20180101”
},
duration: 100,
bytes: 350000000,
query_param_1: 1.0,
query_param_2: “log”,
...
}

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#GHC18
Query Data
Query 1
{
query_time: “20180209 09:00:05”,
user: “user1”,
dataset: “x”,
date_range: {
begin: “20000101”,
end: “20180101”
},
duration: 100,
bytes: 350000000,
query_param_1: 1.0,
query_param_2: “log”,
...
}

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#GHC18
Query Data
Query 1
{
query_time: “20180209 09:00:05”,
user: “user1”,
dataset: “x”,
date_range: {
begin: “20000101”,
end: “20180101”
},
duration: 100,
bytes: 350000000,
query_param_1: 1.0,
query_param_2: “log”,
...
}
Important for product planning
- What features and query parameters do
people use?
- How are queries distributed across data
sets?
- Who are our biggest users in terms of
number of queries and bytes transferred?
- How many distinct users do we have?
- How has all of this changed over time?

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#GHC18
The Challenge of Query-Level Granularity
Query 1

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#GHC18
The Challenge of Query-Level Granularity
Query 1Query 2

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#GHC18
The Challenge of Query-Level Granularity
Query 1Query 2
Query 2
Query 2
Query 2
Query n

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#GHC18
The Challenge of Query-Level Granularity
Query 1Query 2
Query 2
Query 2
Query 2
Query n
time
QueryRate
Bursts up to 50,000
queries/sec

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#GHC18
The End Goal
High performance data
platform
Up to 50,000 queries/sec
1.85 GiB/sec per node
Query 1Query 2
Queries
We need more insight into who is using our data platform
and how it’s being used.
Our goal: collect and analyze usage metrics with query-
query-level granularity without impacting the
performance and reliability of the data platform.

26.
Let’s build the metrics system
#GHC18

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#GHC18
Problem: what to do with metrics data?
Query 1Query 2
Queries
• Store it with flexible schema
• Be able to analyze & visualize the data quickly

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#GHC18
Problem: what to do with metrics data?
Query 1Query 2
Queries
• Store it with flexible schema
• Be able to analyze & visualize the data quickly
---------------- Open Source Offerings ----------------

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#GHC18
How do we pick what OS offering to use?
Checklist:
 Does it have the right features and potential to solve your problem?
 Is it internally available or supported?
 Licensing?
 Is it supported by an active OS community?
 How many active developers?
 When was the most recent commit/pull request?
 Is it extensible? (e.g. plugins, patches)
 Versioning? Backwards compatible changes?

30.
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Product
Allows flexible
data schema?
Data analysis &
visualization?
Internally
available or
accessible at
Two Sigma?
OS community
support?
Extensible?
Stable
versioning?
30
#GHC18
Problem: what to do with metrics data?
---------------- Open Source Offerings ----------------

31.
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Product
Allows flexible
data schema?
Data analysis &
visualization?
Internally
available or
accessible at
Two Sigma?
OS community
support?
Extensible?
Stable
versioning?
31
#GHC18
Problem: what to do with metrics data?
---------------- Open Source Offerings ----------------

32.
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#GHC18
Solution: Elasticsearch
Elasticsearch is an open source platform that can store event data for easy
searching and analysis

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#GHC18
Solution: Elasticsearch + Kibana
Elasticsearch is an open source platform that can store event data for easy
searching and analysis
There are plugins like Kibana, that
make data analysis and
visualization easy.

34.
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#GHC18
Destination for the data is Elasticsearch
We want to get our query data into “indexes” in Elasticsearch. An index per
day makes for easy searching and archiving across time.
metrics-2018-01-01
...
...
metrics-2018-01-02
metrics-2018-01-08
metrics-2018-01-09
Query 1Query 2
Queries

35.
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Elasticsearch
was not built to
handle 50,000
msgs/sec
35
#GHC18
Problem: Elasticsearch can’t handle throughput
We don’t want Elasticsearch performance to hurt the performance of our data
platform.
metrics-2018-01-01
...
...
metrics-2018-01-02
metrics-2018-01-08
metrics-2018-01-09
Query 1Query 2
Queries
???

36.
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#GHC18
Problem: Elasticsearch can’t handle throughput
Idea: use a buffer to handle the throughput bursts
metrics-2018-01-01
...
...
metrics-2018-01-02
metrics-2018-01-08
metrics-2018-01-09
Query 1Query 2
Queries
(Buffer)
Input flow Output flow

37.
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Product
Can handle
throughput
bursts?
Internally available
or accessible at
Two Sigma?
OS community
support?
Extensible?
Stable
versioning?
37
#GHC18
Problem: Elasticsearch can’t handle throughput
---------------- Open Source Offerings ----------------
As-a-
service

38.
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Product
Can handle
throughput
bursts?
Internally available
or accessible at
Two Sigma?
OS community
support?
Extensible?
Stable
versioning?
38
#GHC18
Problem: Elasticsearch can’t handle throughput
---------------- Open Source Offerings ----------------
As-a-
service

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#GHC18
Solution: Kafka
Topic (partitioned queue)
Kafka is an open source
streaming platform that allows
you to
produce data to
&
consume data from
a Kafka topic.
It’s designed for high throughput,
low latency.

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#GHC18
Kafka is more suitable for our throughput
Queries
...
Our data platform
Many asynchronous
Java Client producers
roundrobin
Partition 0
Topic
Kafka can handle the high throughput bursts in data that Elasticsearch couldn’t.
Partition 1
Partition 2
Partition 3
Partition n

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#GHC18
Kafka is more suitable for our throughput
Queries
...
Our data platform
Many asynchronous
Java Client producers
roundrobin
Partition 0
Topic
Kafka can handle the high throughput bursts in data that Elasticsearch couldn’t.
Partition 1
Partition 2
Partition 3
Partition n
(Kafka)
Input flow Output flow

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#GHC18
Kafka as a Buffer
Queries
...
Our data platform
Partition 0
We can use Kafka as an intermediary buffer to store our metrics before writing
to Elasticsearch.
Partition 1
Partition 2
Partition 3
Partition n
metrics-2018-01-01
...
...
metrics-2018-01-02
metrics-2018-01-08
metrics-2018-01-09

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Our data platform
43
#GHC18
Problem: How to get from Kafka to Elasticsearch?
Queries
...
Partition 0
We can use Kafka as an intermediary buffer to store our metrics before writing
to Elasticsearch.
Partition 1
Partition 2
Partition 3
Partition n
...
...
? ? ?
metrics-2018-01-01
metrics-2018-01-02
metrics-2018-01-08
metrics-2018-01-09

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#GHC18
Problem: How to get from Kafka to Elasticsearch?
---------------- Open Source Offerings ----------------
Product
Easy reading
from Kafka?
Easy writing to
Elasticsearch?
Internally available
or accessible at
Two Sigma?
OS community
support?
Extensible?
Stable
versioning?

45.
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#GHC18
Problem: How to get from Kafka to Elasticsearch?
---------------- Open Source Offerings ----------------
Product
Easy reading
from Kafka?
Easy writing to
Elasticsearch?
Internally available
or accessible at
Two Sigma?
OS community
support?
Extensible?
Stable
versioning?

46.
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Our data platform
46
#GHC18
Queries
...
Partition 0
Logstash is an open source data processing pipeline.
It ingests -> transforms -> and “stashes” data.
Partition 1
Partition 2
Partition 3
Partition n
...
...
Solution: Logstash
metrics-2018-01-01
metrics-2018-01-02
metrics-2018-01-08
metrics-2018-01-09

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Our data platform
47
#GHC18
Queries
...
Partition 0
We can use logstash to ingest data from Kafka, transform it as we’d like, and
stash it in elasticsearch.
Partition 1
Partition 2
Partition 3
Partition n
...
...
Solution: Logstash
metrics-2018-01-01
metrics-2018-01-02
metrics-2018-01-08
metrics-2018-01-09

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Our data platform
48
#GHC18
Queries
Logstash connects Kafka to Elasticsearch
input {
kafka {
bootstrap_servers => "kafka-server.host:9095"
topic_id => "usage-topic"
codec => "json"
group_id => "consumer-group-a"
}
}
filter {
date {
match => ["query_time", "UNIX_MS"]
remove_field => ["query_time"]
}
}
output {
elasticsearch {
index => "metrics-%{+YYYY-MM-dd}"
hosts => ["metrics.elasticsearch.host:443"]
}
}
...
Partition 0
Partition 1
Partition 2
Partition 3
Partition n
...
...
metrics-2018-01-01
metrics-2018-01-02
metrics-2018-01-08
metrics-2018-01-09

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Our data platform
49
#GHC18
Queries
Logstash can scale
...
...
metrics-2018-01-01
metrics-2018-01-02
metrics-2018-01-08
metrics-2018-01-09
...
Partition 0
Partition 1
Partition 2
Partition 3
Partition n
Can scale up to the number of partitions in the Kafka topic.
Easy as starting more logstash instances with the same configuration.

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Our data platform
50
#GHC18
Queries
Problem: how to manage many logstash instances?
...
...
metrics-2018-01-01
metrics-2018-01-02
metrics-2018-01-08
metrics-2018-01-09
...
Partition 0
Partition 1
Partition 2
Partition 3
Partition n
With multiple logstash instances, we need a way to manage them.
? ? ?

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#GHC18
Problem: how to manage many logstash instances?
---------------- Open Source Offerings ----------------
Product
Management of
non-web
services?
Minimal
overhead?
Internally available
or accessible at
Two Sigma?
OS community
support?
Extensible?
Stable
versioning?
TS
Waiter

52.
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#GHC18
Problem: how to manage many logstash instances?
---------------- Open Source Offerings ----------------
Product
Management of
non-web
services?
Minimal
overhead?
Internally available
or accessible at
Two Sigma?
OS community
support?
Extensible?
Stable
versioning?
TS
Waiter

53.
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Our data platform
53
#GHC18
Queries
...
Partition 0
Marathon is an open source container orchestration platform.
It schedules, monitors, and restarts applications as needed.
Partition 1
Partition 2
Partition 3
Partition n
...
...
Solution: Marathon
metrics-2018-01-01
metrics-2018-01-02
metrics-2018-01-08
metrics-2018-01-09

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Our data platform
54
#GHC18
Queries
...
Partition 0
We use Marathon to manage our logstash instances.
Partition 1
Partition 2
Partition 3
Partition n
...
...
Marathon keeps logstash instances up and running
metrics-2018-01-01
metrics-2018-01-02
metrics-2018-01-08
metrics-2018-01-09

55.
That was a lot of new tech,
let’s recap.
#GHC18

56.
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#GHC18
The Complete Metrics System
Our data platform
Queries
...
Partition 0
Partition 1
Partition 2
Partition 3
Partition n
...
...
metrics-2018-01-01
metrics-2018-01-02
metrics-2018-01-08
metrics-2018-01-09
Data platform with up
to 50,000 queries/sec
Kafka as a high
throughput, low
latency buffer
Logstash instances running on
marathon ingesting Kafka data and
stashing it in Elasticsearch
Elasticsearch indexes
our metrics and we can
analyze it using Kibana!

57.
But wait, why did we use open
source solutions in the first
place?
#GHC18

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#GHC18
The Alternatives
• Write your own code from scratch
• Support burden is all on you
• Use internal solutions if they exist
• E.g. Your company may choose not to develop custom solutions for process
management if it’s business goals and strengths are more in the domain of data
analysis and modeling.

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#GHC18
Open Source Benefits
• Use solutions that have been tested and developed by a community of contributors
and users
• Save developer time
• I was able to design, deliver, & deploy this metrics system in under a month
• Can give back to the OS community. E.g. if you find a bug or missing feature
• Report the issue
• Make a contribution

60.
Results
#GHC18

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#GHC18
Metrics we’ve seen with our new system
Sum of bytes aggregated by data set
% distribution of
‘dataset’ query
parameter
Dataset test_data_x

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#GHC18
Metrics we’ve seen with our new system
Number of unique users per dataset
Number of unique users over time
Dataset

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#GHC18
Metrics we’ve seen with our new system

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#GHC18
Engineering Lessons Learned
• Requirements shape the system you build
• Problem-solution approach
• Use open source tools when you can
• Why reinvent the wheel?
• Support from the open source community
• BUT be mindful when choosing open source solutions
• Sometimes it’s about orchestrating the pieces together
• Configurations are important

65.
Thanks for listening!
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Visit https://opensource.twosigma.com/
Email me at Hyonjee.Joo@twosigma.com
#GHC18