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DIAGNOSING OPEN-
SOURCE COMMUNITY
HEALTH WITH SPARK
William Benton

Red Hat Emerging Technology

willb@redhat.com
BACKGROUND
FEDORA MESSAGING
BULK DATA INGEST
ANALYZING COMMUNITY
NEXT STEPS
2
BACKGROUND
BACKGROUND
FEDORA MESSAGING
BULK DATA INGEST
ANALYZING COMMUNITY
NEXT STEPS
2
BACKGROUND
WHAT MAKES A GREAT
OPEN-SOURCE COMMUNITY?
3
4
QUICK FACTS ABOUT FEDORA
• Seven architectures

• ~17k packages (in Fedora 22)

• Tens of thousands of contributors

• Millions of users

• Many moving parts in Fedora infrastructure
5
BACKGROUND
FEDORA MESSAGING
BULK DATA INGEST
ANALYZING COMMUNITY
NEXT STEPS
6
7
fedmsg bus
7
fedmsg bus
package
SCM
fedmsg-hub
ansible
MediaWiki
(via mod_php)
COPR and
secondary
arch builds
7
fedmsg bus
package
SCM
fedmsg-hub
ansible
MediaWiki
(via mod_php)
COPR and
secondary
arch builds
meetbot
7
fedmsg bus
package
SCM
fedmsg-hub
ansible
MediaWiki
(via mod_php)
COPR and
secondary
arch builds
meetbot koji
7
fedmsg bus
package
SCM
fedmsg-hub
ansible
MediaWiki
(via mod_php)
COPR and
secondary
arch builds
meetbot koji Apache mod_wsgi
bodhi
accounts
ticketing
package
database
…many
others!
7
fedmsg bus
package
SCM
fedmsg-hub
ansible
MediaWiki
(via mod_php)
COPR and
secondary
arch builds
meetbot koji Apache mod_wsgi
bodhi
accounts
ticketing
package
database
…many
others!
IRC
gateway
CLI tools
web
gateway
#fedora-fedmsg
7
fedmsg bus
package
SCM
fedmsg-hub
ansible
MediaWiki
(via mod_php)
COPR and
secondary
arch builds
meetbot koji Apache mod_wsgi
bodhi
accounts
ticketing
package
database
…many
others!
IRC
gateway
CLI tools
web
gateway
notifications
#fedora-fedmsg
7
fedmsg bus
package
SCM
fedmsg-hub
ansible
MediaWiki
(via mod_php)
COPR and
secondary
arch builds
meetbot koji Apache mod_wsgi
bodhi
accounts
ticketing
package
database
…many
others!
IRC
gateway
CLI tools
web
gateway
notifications
datanommer
(warehouse)
#fedora-fedmsg
7
fedmsg bus
package
SCM
fedmsg-hub
ansible
MediaWiki
(via mod_php)
COPR and
secondary
arch builds
meetbot koji Apache mod_wsgi
bodhi
accounts
ticketing
package
database
…many
others!
IRC
gateway
CLI tools
web
gateway
notifications
Fedora
badges
datanommer
(warehouse)
#fedora-fedmsg
8
BACKGROUND
FEDORA MESSAGING
BULK DATA INGEST
ANALYZING COMMUNITY
NEXT STEPS
9
MESSAGE SOURCES
• Bulk historical data (PostgreSQL dump)

• Specific historical records (REST/JSON)

• Streaming messages (∅MQ)
10
11
CREATE TABLE messages (
id integer NOT NULL,
i integer NOT NULL,
"timestamp" timestamp NOT NULL,
certificate text,
signature text,
topic text,
_msg text NOT NULL,
category text,
source_name text,
source_version text,
msg_id text
);
CREATE TABLE messages (
id integer NOT NULL,
i integer NOT NULL,
"timestamp" timestamp NOT NULL,
certificate text,
signature text,
topic text,
_msg text NOT NULL,
category text,
source_name text,
source_version text,
msg_id text
);
11
CREATE TABLE
i
certificate
signature
topic
_msg
category
source_name
source_version
msg_id
);
CREATE TABLE messages (
id integer NOT NULL,
i integer NOT NULL,
"timestamp" timestamp NOT NULL,
certificate text,
signature text,
topic text,
_msg text NOT NULL,
category text,
source_name text,
source_version text,
msg_id text
);
“I’LL JUST USE JSON!”
12
{
"timestamp": "now",
"you-have":
[{"problems": 2}]
}
JSON AND SCHEMA INFERENCE
13
{
"animals" : {
"duck" :
{ "mammal": false, "bill": true, "eggs": true },
"zebra" :
{ "mammal": true, "bill": false, "eggs": false },
"platypus" :
{ "mammal": true, "bill": true, "eggs": true }
}
}
JSON AND SCHEMA INFERENCE
13
{
"animals" : [
{"k": "duck",
"v": { "mammal": false,
"bill": true, "eggs": true } },
{"k": "zebra",
"v": { “mammal": true,
"bill": false, "eggs": false },
/* ... */
]
}
DIVERGING SCHEMAS
14
{ "branches" : ["f22", "master"] }
{ "branches" :
[ { "name": "f22", "commit" : "05afffa1" } ]
}
PREPROCESSING WITH JSON4S
15
// use json4s and partial functions
// see the Silex library for an easy interface!
def renameBranches(msg: JValue) = {
msg transformField {
case JField("branches", v@JArray(JString(_)::_)) =>
("pkg_branches", v)
case JField("branches", v@JArray(JObject(_)::_)) =>
("git_branches", v)
}
}
BACKGROUND
FEDORA MESSAGING
BULK DATA INGEST
ANALYZING COMMUNITY
NEXT STEPS
16
MESSAGE COUNT DISTRIBUTION
17
100%
40%
60%
80%
10 103 107
message counts (log scale)
userswithatmost
105
UNIQUE TOPIC DISTRIBUTION
18
100%
40%
60%
80%
5 10 15 20 25
unique topics
userswithatmost
TOPIC SETS AS FEATURES
• Idea: characterize users by the sets of
unique message topics affecting them

• Use ML Pipeline transformers to go from
sets (modeled as arrays) to feature vectors
19
TOPIC SETS AS FEATURES
20
// expose this Spark-private DataType
package org.apache.spark.hacks {
type VectorType =
org.apache.spark.mllib.linalg.VectorUDT
}
TOPIC SETS AS FEATURES
21
trait SVParams extends Params {
val inputCol =
new Param[String](this, "inputCol", "...")
val outputCol =
new Param[String](this, "outputCol", "...")
val vecSize =
new IntParam(this, "vecSize", "...")
// ...
}
TOPIC SETS AS FEATURES
21
trait SVParams extends Params {
// ...
def pvals(pm: ParamMap) = ( // 1.3-specific
paramMap.get(inputCol).getOrElse("topicSet"),
paramMap.get(outputCol).getOrElse("features"),
paramMap.get(vecSize).getOrElse(128)
)
}
22
class SetVectorizer(override val uid: String)
extends Transformer with SVParams {
// hopefully VectorUDT will be public soon!
final val VT = new org.apache.spark.hacks.VectorType()
def transformSchema(schema: StructType, params: ParamMap) = {
val outc = paramMap.get(outputCol).getOrElse("features")
StructType(schema.fields ++ Seq(StructField(outc, VT, true)))
}
def transform(df: DataFrame, params: ParamMap) = {
val (inc, outc, vs) = pvals(paramMap)
df.withColumn(outc, callUDF({ a: Seq[Int] =>
Vectors.sparse(vs, a.toArray, Array.fill(a.size)(1.0))
}, VT, df(inc)))
}
}
CLASSIFYING FEDORA USERS
• Problem: can we identify Fedora packager
sponsors by their message topic sets?

• Logistic regression was easy to try

• ~1% prediction error rate…but only ~10%
of actual sponsors were predicted as such!
23
A BETTER APPROACH
24
Building a decision tree identified the following
message topics as likely to predict that someone was a
Fedora packager sponsor:
• org.fedora.prod.fas.role.update
• org.fedora.prod.fas.user.update
BACKGROUND
FEDORA MESSAGING
BULK DATA INGEST
ANALYZING COMMUNITY
NEXT STEPS
25
WHAT’S NEXT
• More about fedmsg: http://fedmsg.com

• Our Silex library includes helpers for JSON
data ingest: http://silex.freevariable.com

• More details about this project:

http://chapeau.freevariable.com1
26
1 http://chapeau.freevariable.com/2015/06/summit-fedmsg.html
THANKS!
27

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