Joining multiple disparate data-sources, commonly dubbed Master-Data-Management (MDM), is usually not a fun exercise. I would like to show you how using a graph database (Neo4j) and an interesting dataset (developer-oriented collaboration sites) to put the fun back into MDM. This approach will allow you to quickly and sensibly merge data from different sources into a consistent picture and query across the data efficiently to answer your most pressing questions.
You can read the associated blog posts on my blog. The blog posts cover the hows and whys of the project, while this and other GraphGists will examine how to answer specific questions of the data.
The first step won’t have much to do with Master Data Managment. Here I’ve imported data from StackOverflow's API in order to start asking questions about what the site could do more easily with a Neo4j database available to it.
I’ve created a Clojure project to import questions, tags, answers, and associated users into Neo4j. This gives me the following model:
I imported about 1000 questions from the API, but since GraphGists render in the browser I’ve limited the dataset here. Be warned that this GraphGist may still take some time to completely render
create constraint on (n:`Answer`) assert n.`answer_id` is unique;
create constraint on (n:`Question`) assert n.`question_id` is unique;
create constraint on (n:`Tag`) assert n.`text` is unique;
create constraint on (n:`User`) assert n.`user_id` is unique;
create (_1251:`Question` {`answer_count`:1, `creation_date`:1423027961, `is_answered`:true, `last_activity_date`:1423549857, `link`:"http://stackoverflow.com/questions/28314155/slow-aggregation-on-big-neo4j-graph", `question_id`:28314155, `score`:1, `title`:"Slow aggregation on big neo4j graph", `view_count`:51})
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create _2035-[:`ANSWERS`]->_1845
create _2036-[:`PROVIDED`]->_2035
create _2037-[:`ANSWERS`]->_1847
create _2038-[:`ANSWERS`]->_1849
create _2039-[:`PROVIDED`]->_2480
create _2039-[:`PROVIDED`]->_2479
create _2039-[:`PROVIDED`]->_2473
create _2039-[:`PROVIDED`]->_2269
create _2039-[:`PROVIDED`]->_2038
create _2040-[:`ANSWERS`]->_1853
create _2041-[:`ANSWERS`]->_1854
create _2042-[:`ANSWERS`]->_1847
create _2043-[:`ANSWERS`]->_1829
create _2044-[:`ANSWERS`]->_1847
create _2045-[:`ASKED`]->_3586
create _2045-[:`ASKED`]->_3570
create _2045-[:`ASKED`]->_3513
create _2045-[:`ASKED`]->_3270
create _2045-[:`PROVIDED`]->_2044
create _2046-[:`ANSWERS`]->_1861
create _2047-[:`ASKED`]->_3118
create _2047-[:`ASKED`]->_2882
create _2047-[:`PROVIDED`]->_3635
create _2047-[:`PROVIDED`]->_3624
create _2047-[:`PROVIDED`]->_3414
create _2047-[:`PROVIDED`]->_2720
create _2047-[:`PROVIDED`]->_2715
create _2047-[:`PROVIDED`]->_2711
create _2047-[:`PROVIDED`]->_2705
create _2047-[:`PROVIDED`]->_2046
create _2048-[:`ANSWERS`]->_1858
create _2049-[:`ANSWERS`]->_1864
create _2050-[:`ANSWERS`]->_1858
create _2051-[:`PROVIDED`]->_2478
create _2051-[:`PROVIDED`]->_2050
create _2052-[:`ANSWERS`]->_1868
create _2053<-[:`TAGS`]-_1342
create _2053<-[:`TAGS`]-_2055
create _2053<-[:`TAGS`]-_1280
create _2053<-[:`TAGS`]-_1254
create _2054-[:`ASKED`]->_2053
create _2056<-[:`TAGS`]-_2057
create _2056<-[:`TAGS`]-_1254
create _2056<-[:`TAGS`]-_1613
create _2058<-[:`TAGS`]-_1255
create _2058<-[:`TAGS`]-_1254
create _2059-[:`ASKED`]->_2058
create _2060<-[:`TAGS`]-_1325
create _2060<-[:`TAGS`]-_1254
create _2060<-[:`TAGS`]-_1324
create _2061-[:`ASKED`]->_2177
create _2061-[:`ASKED`]->_2060
create _2061-[:`PROVIDED`]->_2275
create _2062<-[:`TAGS`]-_1254
create _2063-[:`ASKED`]->_2564
create _2063-[:`ASKED`]->_2542
create _2063-[:`ASKED`]->_2333
create _2063-[:`ASKED`]->_2062
create _2063-[:`PROVIDED`]->_2728
create _2064<-[:`TAGS`]-_2067
create _2064<-[:`TAGS`]-_1285
create _2064<-[:`TAGS`]-_2066
create _2064<-[:`TAGS`]-_1254
create _2064<-[:`TAGS`]-_1429
create _2065-[:`ASKED`]->_2064
create _2068<-[:`TAGS`]-_2071
create _2068<-[:`TAGS`]-_1356
create _2068<-[:`TAGS`]-_1254
create _2068<-[:`TAGS`]-_2070
create _2068<-[:`TAGS`]-_1692
create _2069-[:`ASKED`]->_2241
create _2069-[:`ASKED`]->_2141
create _2069-[:`ASKED`]->_2068
create _2072<-[:`TAGS`]-_1254
create _2072<-[:`TAGS`]-_2074
create _2072<-[:`TAGS`]-_1259
create _2072<-[:`TAGS`]-_1258
create _2073-[:`ASKED`]->_2072
create _2077<-[:`TAGS`]-_1485
create _2077<-[:`TAGS`]-_1342
create _2077<-[:`TAGS`]-_1254
create _2077<-[:`TAGS`]-_1512
create _2078-[:`ASKED`]->_2077
create _2079<-[:`TAGS`]-_1281
create _2079<-[:`TAGS`]-_1254
create _2080-[:`ASKED`]->_2079
create _2080-[:`PROVIDED`]->_2259
create _2085<-[:`TAGS`]-_1254
create _2085<-[:`TAGS`]-_1723
create _2085<-[:`TAGS`]-_1258
create _2086-[:`ASKED`]->_2085
create _2087<-[:`TAGS`]-_1255
create _2087<-[:`TAGS`]-_1254
create _2088-[:`ASKED`]->_2087
create _2088-[:`PROVIDED`]->_2263
create _2089<-[:`TAGS`]-_1356
create _2089<-[:`TAGS`]-_1254
create _2089<-[:`TAGS`]-_2091
create _2089<-[:`TAGS`]-_1692
create _2090-[:`ASKED`]->_2089
create _2094<-[:`TAGS`]-_1356
create _2094<-[:`TAGS`]-_2096
create _2094<-[:`TAGS`]-_1336
create _2094<-[:`TAGS`]-_1254
create _2094<-[:`TAGS`]-_1370
create _2095-[:`ASKED`]->_2094
create _2097<-[:`TAGS`]-_1255
create _2097<-[:`TAGS`]-_1254
create _2098<-[:`TAGS`]-_1255
create _2098<-[:`TAGS`]-_1254
create _2098<-[:`TAGS`]-_1253
create _2099-[:`ASKED`]->_2098
create _2100<-[:`TAGS`]-_1254
create _2101<-[:`TAGS`]-_1255
create _2101<-[:`TAGS`]-_1254
create _2102-[:`ASKED`]->_2101
create _2103<-[:`TAGS`]-_1255
create _2103<-[:`TAGS`]-_1254
create _2104-[:`ASKED`]->_2103
create _2105<-[:`TAGS`]-_1764
create _2105<-[:`TAGS`]-_1285
create _2105<-[:`TAGS`]-_1254
create _2106<-[:`TAGS`]-_1342
create _2106<-[:`TAGS`]-_1254
create _2106<-[:`TAGS`]-_2108
create _2106<-[:`TAGS`]-_1336
create _2106<-[:`TAGS`]-_1335
create _2107-[:`ASKED`]->_2106
create _2109<-[:`TAGS`]-_1254
create _2109<-[:`TAGS`]-_1500
create _2110<-[:`TAGS`]-_1255
create _2110<-[:`TAGS`]-_1254
create _2110<-[:`TAGS`]-_1321
create _2111<-[:`TAGS`]-_1254
create _2111<-[:`TAGS`]-_2113
create _2112-[:`ASKED`]->_2111
create _2114<-[:`TAGS`]-_1254
create _2115-[:`ASKED`]->_2114
create _2116<-[:`TAGS`]-_2119
create _2116<-[:`TAGS`]-_1342
create _2116<-[:`TAGS`]-_2118
create _2116<-[:`TAGS`]-_1254
create _2117-[:`ASKED`]->_2116
create _2250-[:`ANSWERS`]->_2053
create _2251-[:`ANSWERS`]->_2056
create _2252-[:`ANSWERS`]->_2060
create _2253-[:`ANSWERS`]->_2062
create _2254-[:`ANSWERS`]->_2064
create _2255-[:`ANSWERS`]->_2068
create _2256-[:`ANSWERS`]->_2072
create _2257-[:`ANSWERS`]->_2077
create _2258-[:`PROVIDED`]->_2257
create _2259-[:`ANSWERS`]->_2079
create _2260-[:`ANSWERS`]->_2079
create _2261-[:`ANSWERS`]->_2077
create _2262-[:`ANSWERS`]->_2085
create _2263-[:`ANSWERS`]->_2087
create _2264-[:`ANSWERS`]->_2089
create _2265-[:`PROVIDED`]->_2264
create _2266-[:`ANSWERS`]->_2094
create _2267-[:`ANSWERS`]->_2097
create _2268-[:`ANSWERS`]->_2098
create _2269-[:`ANSWERS`]->_2100
create _2270-[:`ANSWERS`]->_2101
create _2271-[:`ANSWERS`]->_2103
create _2272-[:`ANSWERS`]->_2058
create _2273-[:`ANSWERS`]->_2058
create _2274-[:`ANSWERS`]->_2105
create _2275-[:`ANSWERS`]->_2106
create _2276-[:`ANSWERS`]->_2109
create _2277-[:`ANSWERS`]->_2109
create _2278-[:`ANSWERS`]->_2110
create _2279-[:`ANSWERS`]->_2111
create _2280-[:`ANSWERS`]->_2114
create _2281-[:`PROVIDED`]->_2282
create _2281-[:`PROVIDED`]->_2280
create _2282-[:`ANSWERS`]->_2116One of the first things that I thought of was creating a way to suggest tags to a user while they are filling out a question. The following query finds all questions which have both the neo4j and python tags and then aggregates the most popular tags on those questions.
Related tags for python
MATCH (question:Question)<-[:TAGS]-(tag:Tag)
WHERE tag.text IN ['neo4j', 'python']
WITH question, count(DISTINCT tag) AS count WHERE count = 2 WITH question
MATCH question<-[:TAGS]-(tag:Tag)
WHERE NOT(tag.text IN ['neo4j', 'python'])
RETURN tag.text, COUNT(tag) ORDER BY COUNT(tag) DESCIf this were a query in an application we would replace the two occurrences of the array of tags with a parameter.
py2neo |
71 |
cypher |
32 |
bulbs |
20 |
django |
19 |
neo4django |
11 |
Because the tags are attached to the question and not the answer, it would normally be a bit harder to query for what tags users are answering for. Of course, an extra relationship isn’t much of a problem for Neo4j.
Say we wanted the StackOverflow application to find topic experts based on given tags. This might be useful if the question was going unanswered and we wanted the system to do a targeted E-Mail to experts to see if they would be interested in contributing. Here is how you would do that in Neo4j:
Finding experts
MATCH (question:Question)<-[:TAGS]-(tag:Tag)
WHERE tag.text IN ['neo4j', 'python']
WITH question, count(DISTINCT tag) AS count WHERE count = 2 WITH question
MATCH question<-[:ANSWERS]-(answer:Answer)<-[:PROVIDED]-(user:User)
RETURN user.display_name, count(answer)
ORDER BY count(answer) DESCNigel Small |
15 |
espeed |
5 |
Peter Neubauer |
3 |
Javier de la Rosa |
2 |
Wes Freeman |
2 |
If we wanted to look across a number of users to find out what subjects they are knowledgable in, we could query like this:
Finding expertise
MATCH (user:User)-[:PROVIDED]->(answer:Answer)-[:ANSWERS]->(:Question)<-[:TAGS]-(tag:Tag)
WITH user, tag, count(answer.score) AS count
ORDER BY count DESC
WHERE count > 1 AND NOT(tag.text IN ['neo4j', 'cypher'])
RETURN user.display_name, collect({ count: count, tag: tag.text })
LIMIT 10F.O.O |
[{count: 2, tag: "spring-data-neo4j"}] |
agpt |
[{count: 2, tag: "spring-data-neo4j"},{count: 2, tag: "java"}] |
ean5533 |
[{count: 2, tag: "java"}] |
stephen mallette |
[{count: 11, tag: "gremlin"},{count: 6, tag: "graph-databases"},{count: 5, tag: "tinkerpop"},{count: 4, tag: "titan"},{count: 2, tag: "orient-db"}] |
Javier de la Rosa |
[{count: 2, tag: "neo4j.py"},{count: 2, tag: "python"}] |
bendaizer |
[{count: 2, tag: "graph-databases"},{count: 2, tag: "nosql"}] |
funglejunk |
[{count: 2, tag: "java"}] |
MYK |
[{count: 2, tag: "ajax"},{count: 2, tag: "jquery"}] |
TobyTheDog |
[{count: 2, tag: "database"}] |
lassewesth |
[{count: 3, tag: "spring-data-neo4j"},{count: 2, tag: "spring-security"}] |
Maybe we want to find out if one user has often answered question for another user.
Potential "mentor" relationships
MATCH (answerer:User)-[:PROVIDED]->(a:Answer)-[:ANSWERS]->(q:Question)<-[:ASKED]-(asker:User)
RETURN asker.display_name, answerer.display_name, count(q)
ORDER BY count(q) DESC| asker.display_name | answerer.display_name | count(q) |
|---|---|---|
Clam |
subvertallchris |
5 |
deemeetree |
Stefan Armbruster |
4 |
salvador |
Stefan Armbruster |
3 |
red-devil |
Michael Hunger |
3 |
penta |
Stefan Armbruster |
3 |
And those are a few ways which we can explore the StackOverflow model with Neo4j.
Soon I will be developing more resources on how to combine this StackOverflow data with other datasources. Stay tuned to my blog!