Applikationsentwicklung mit MongoDB: Teil 5: Reporting & Aggregation

1. Solutions Architect, MongoDB
Marc Schwering
#MongoDBBasics @MongoDB @m4rcsch
Applikationsentwicklung mit MongoDB
Reporting & Aggregation

2. 2
• Recap from last session
• Reporting / Analytics options
• Map Reduce
• Aggregation Framework introduction
– Aggregation explain
• mycms application reports
• Geospatial with Aggregation Framework
• Text Search with Aggregation Framework
Agenda

3. 3
• Virtual Genius Bar
– Use the chat to post
questions
– EMEA Solution
Architecture / Support
team are on hand
– Make use of them
during the sessions!!!
Q & A

4. Recap from last time….

5. 5
Indexing
• Indexes
• Multikey, compound,
‘dot.notation’
• Covered, sorting
• Text, GeoSpatial
• Btrees
>db.articles.ensureIndex( { author
: 1, tags : 1 } )
>db.user.find({user:"danr"}, {_id:0,
password:1})
>db.articles.ensureIndex( {
location: “2dsphere” } )
>>db.articles.ensureIndex(
{ "$**" : “text”,
name : “TextIndex”} )
options db.col.ensureIndex({ key : type})

6. 6
Index performance / efficiency
• Examine index plans
• Identity slow queries
• n / nscanned ratio
• Which index used.
operators .explain() , db profiler
> db.articles.find(
{author:'Dan Roberts’})
.sort({date:-1}
).explain()
> db.setProfilingLevel(1,
100)
{ "was" : 0, "slowms" : 100,
"ok" : 1 }
> db.system.profile.find()
.pretty()

7. Reporting / Analytics options

8. 8
• Query Language
– Leverage pre aggregated documents
• Aggregation Framework
– Calculate new values from the data that we have
– For instance : Average views, comments count
• MapReduce
– Internal Javascript based implementation
– External Hadoop, using the MongoDB connector
• A combination of the above
Access data for reporting, options

9. 9
• Immediate results
– Simple from a query
perspective.
– Interactions collection
Pre Aggregated Reports
{
‘_id’ : ObjectId(..),
‘article_id’ : ObjectId(..),
‘section’ : ‘schema’,
‘date’ : ISODate(..),
‘daily’: { ‘views’ : 45,
‘comments’ : 150 }
‘hours’ : {
0 : { ‘views’ : 10 },
1 : { ‘views’ : 2 },
…
23 : { ‘views’ : 14,
‘comments’ : 10 }
}
}
> db.interactions.find(
{"article_id" : ObjectId(”…..")},
{_id:0, hourly:1}
)

10. 10
• Use query result to display directly in application
– Create new REST API
– D3.js library or similar in UI
Pre Aggregated Reports
{
"hourly" : {
"0" : {
"view" : 1
},
"1" : {
"view" : 1
},
……
"22" : {
"view" : 5
},
"23" : {
"view" : 3
}
}
}

11. Map Reduce

12. 12
• Map Reduce
– MongoDB – JavaScript
• Incremental Map Reduce
Map Reduce
//Map Reduce Example
> db.articles.mapReduce(
function() { emit(this.author, this.comment_count); },
function(key, values) { return Array.sum (values) },
{
query : {},
out: { merge: "comment_count" }
}
)
Output
{ "_id" : "Dan Roberts", "value" : 6 }
{ "_id" : "Jim Duffy", "value" : 1 }
{ "_id" : "Kunal Taneja", "value" : 2 }
{ "_id" : "Paul Done", "value" : 2 }

13. 13
MongoDB – Hadoop Connector
Hadoop Integration
Primary
Secondary
Secondary
HDFS
Primary
Secondary
Secondary
Primary
Secondary
Secondary
Primary
Secondary
Secondary
HDFS HDFS HDFS
MapReduce MapReduce MapReduce MapReduce
MongoS MongoSMongoS
Application ApplicationApplication
Application
Dash Boards /
Reporting
1) Data Flow,
Input /
Output via
Application
Tier

14. Aggregation Framework

15. 15
• Multi-stage pipeline
– Like a unix pipe –
• “ps -ef | grep mongod”
– Aggregate data, Transform
documents
– Implemented in the core server
Aggregation Framework
//Find out which are the most popular tags…
db.articles.aggregate([
{ $unwind : "$tags" },
{ $group : { _id : "$tags" , number : { $sum : 1 } } },
{ $sort : { number : -1 } }
])
Output
{ "_id" : "mongodb", "number" : 6 }
{ "_id" : "nosql", "number" : 3 }
{ "_id" : "database", "number" : 1 }
{ "_id" : "aggregation", "number" : 1 }
{ "_id" : "node", "number" : 1 }

16. 16
In our mycms application..
//Our new python example
@app.route('/cms/api/v1.0/tag_counts', methods=['GET'])
def tag_counts():
pipeline = [ { "$unwind" : "$tags" },
{ "$group" : { "_id" : "$tags" , "number" : { "$sum" : 1 } }
},
{ "$sort" : { "number" : -1 } }]
cur = db['articles'].aggregate(pipeline, cursor={})
# Check everything ok
if not cur:
abort(400)
# iterate the cursor and add docs to a dict
tags = [tag for tag in cur]
return jsonify({'tags' : json.dumps(tags, default=json_util.default)})

17. 17
• Pipeline and Expression operators
Aggregation operators
Pipeline
$match
$sort
$limit
$skip
$project
$unwind
$group
$geoNear
$text
$search
Tip: Other operators for date, time, boolean and string manipulation
Expression
$addToSet
$first
$last
$max
$min
$avg
$push
$sum
Arithmetic
$add
$divide
$mod
$multiply
$subtract
Conditional
$cond
$ifNull
Variables
$let
$map

18. 18
• What reports and analytics do we need in our
application?
– Popular Tags
– Popular Articles
– Popular Locations – integration with Geo Spatial
– Average views per hour or day
Application Reports

19. 19
• Unwind each ‘tags’ array
• Group and count each one, then Sort
• Output to new collection
– Query from new collection so don’t need to compute for
every request.
Popular Tags
db.articles.aggregate([
{ $unwind : "$tags" },
{ $group : { _id : "$tags" , number : { $sum : 1 } } },
{ $sort : { number : -1 } },
{ $out : "tags"}
])

20. 20
• Top 5 articles by average daily views
– Use the $avg operator
– Use use $match to constrain data range
• Utilise with $gt and $lt operators
Popular Articles
db.interactions.aggregate([
{
{$match : { date :
{ $gt : ISODate("2014-02-20T00:00:00.000Z")}}},
{$group : {_id: "$article_id", a : { $avg : "$daily.view"}}},
{$sort : { a : -1}},
{$limit : 5}
]);

21. 21
• Use Explain plan to ensure the efficient use of the
index when querying.
Aggregation Framework Explain
db.interactions.aggregate([
{$group : {_id: "$article_id", a : { $avg : "$daily.view"}}},
{$sort : { a : -1}},
{$limit : 5}
],
{explain : true}
);

22. 22
Explain output…
{
"stages" : [
{
"$cursor" : { "query" : … }, "fields" : { … },
"plan" : {
"cursor" : "BasicCursor",
"isMultiKey" : false,
"scanAndOrder" : false,
"allPlans" : [
{
"cursor" : "BasicCursor",
"isMultiKey" : false,
"scanAndOrder" : false
}
]
}
}
},
…
"ok" : 1
}

23. Geo Spatial & Text Search
Aggregation

24. 24
• $text operator with aggregation framework
– All articles with MongoDB
– Group by author, sort by comments count
Text Search
db.articles.aggregate([
{ $match: { $text: { $search: "mongodb" } } },
{ $group: { _id: "$author", comments:
{ $sum: "$comment_count" } } }
{$sort : {comments: -1}},
])

25. 25
• $geoNear operator with aggregation framework
– Again use geo operator in the $match statement.
– Group by author, and article count.
Utilise with Geo spatial
db.articles.aggregate([
{ $match: { location: { $geoNear :
{ $geometry :
{ type: "Point" ,coordinates : [-0.128, 51.507] } },
$maxDistance :5000}
}
},
{ $group: { _id: "$author", articleCount: { $sum: 1 } } }
])

26. Summary

27. 27
• Aggregating Data…
– Map Reduce
– Hadoop
– Pre-Aggregated Reports
– Aggregation Framework
• Tune with Explain plan
• Compute on the fly or Compute and store
• Geospatial
• Text Search
Summary

28. 28
– MongoDB World Recap!
– Preview into the operation Series.
Next Session – 9th July