Hadoop - MongoDB Webinar June 2014
-
Upload
mongodb -
Category
Technology
-
view
113 -
download
2
description
Transcript of Hadoop - MongoDB Webinar June 2014
Mongo-Hadoop IntegrationJustin Lee
Software Engineer @ MongoDB
We will cover:
•what it is•how it works•a tour of what it can do
A quick briefing on what Mongo and Hadoop are all about:
(Q+A at the end)
document-oriented database with dynamic schema
stores data in JSON-like documents:
{ _id : “kosmo kramer”,
age : 42,location : {
state : ”NY”,zip : ”10024”
},favorite_colors : [“red”, “green”]
}
different structure in each documentvalues can be simple like strings and ints or nested documents
mongodb scales horizontally via sharding to handle lots of data and load
app
Java-based framework for MapReduce
Excels at batch processing on large data setsby taking advantage of parallelism
map reduce created by google (white paper)implemented in open source by hadoop
Mongo-Hadoop Connector - Why
Lots of people using Hadoop and Mongo separately but need integration
Custom import/export scripts often used to get data in+out
Scalability and flexibility with changes in Hadoop or MongoDB configurations
Need to process data across multiple sources
custom scripts slow, fragile
Mongo-Hadoop Connector
Turn MongoDB into a Hadoop-enabled filesystem:use as the input or output for Hadoop
.BSON
-or-
input data
.BSON
-or-
Hadoop Cluster
outputresults
bson file new in 1.1bson is the output of mongodump
Mongo-Hadoop ConnectorBenefits + Features
Takes advantage of full multi-core parallelism to process data in Mongo
Full integration with Hadoop and JVM ecosystems
Can be used with Amazon Elastic MapReduce
Can read and write backup files from local filesystem, HDFS, or S3
Mongo-Hadoop Connector
Vanilla Java MapReduce
write MapReduce code in
ruby
or if you don’t want to use Java,support for Hadoop Streaming.
Benefits + Features
can write your own language binding
Mongo-Hadoop Connector
Support for Pighigh-level scripting language for data analysis and
building MapReduce workflows
Support for HiveSQL-like language for ad-hoc queries + analysis of data sets on
Hadoop-compatible file systems
Benefits + Features
Mongo-Hadoop Connector
How it works:
Adapter examines the MongoDB input collection and calculates a set of splits from the data
Each split gets assigned to a node in Hadoop cluster
In parallel, Hadoop nodes pull data for splits from MongoDB (or BSON) and process them locally
Hadoop merges results and streams output back to MongoDB or BSON
Tour of Mongo-Hadoop, by Example
- Using Java MapReduce with Mongo-Hadoop
- Using Hadoop Streaming
- Pig and Hive with Mongo-Hadoop
- Elastic MapReduce + BSON
{ "_id" : ObjectId("4f2ad4c4d1e2d3f15a000000"), "body" : "Here is our forecast\n\n ", "filename" : "1.", "headers" : { "From" : "[email protected]", "Subject" : "Forecast Info", "X-bcc" : "", "To" : "[email protected]", "X-Origin" : "Allen-P", "X-From" : "Phillip K Allen", "Date" : "Mon, 14 May 2001 16:39:00 -0700 (PDT)", "X-To" : "Tim Belden ", "Message-ID" : "<18782981.1075855378110.JavaMail.evans@thyme>", "Content-Type" : "text/plain; charset=us-ascii", "Mime-Version" : "1.0" }}
Input Data: Enron e-mail corpus (501k records, 1.75Gb)
each document is one email
sender
recipients
{"_id": {"t":"[email protected]", "f":"[email protected]"}, "count" : 14}
{"_id": {"t":"[email protected]", "f":"[email protected]"}, "count" : 9}
{"_id": {"t":"[email protected]", "f":"[email protected]"}, "count" : 99}
{"_id": {"t":"[email protected]", "f":"[email protected]"}, "count" : 48}
{"_id": {"t":"[email protected]", "f":"[email protected]"}, "count" : 20}
Let’s use Hadoop to build a graph of (senders → recipients) and the count of messages exchanged between each pair
bob
alice
eve
charlie
1499
9
48
20
sample, simplified datanodes are people. edges/arrows # of msgs from A to B
Example 1 - Java MapReduce
mongodb document passed into Hadoop MapReduce
Map phase - each input doc gets passed through a Mapper function
@Overridepublic void map(NullWritable key, BSONObject val, final Context context){ BSONObject headers = (BSONObject)val.get("headers"); if(headers.containsKey("From") && headers.containsKey("To")){ String from = (String)headers.get("From"); String to = (String)headers.get("To"); String[] recips = to.split(","); for(int i=0;i<recips.length;i++){ String recip = recips[i].trim(); context.write(new MailPair(from, recip), new IntWritable(1)); } }}
input value doc from mongo. connector will handle translation into BSONObject for you
output written back to MongoDB
Example 1 - Java MapReduce (cont)
Reduce phase - outputs of Map are grouped together by key and passed to Reducer
the {to, from} key
list of all the values collected under the key
public void reduce( final MailPair pKey, final Iterable<IntWritable> pValues, final Context pContext ){ int sum = 0;
for ( final IntWritable value : pValues ){ sum += value.get(); }
BSONObject outDoc = new BasicDBObjectBuilder().start() .add( "f" , pKey.from)
.add( "t" , pKey.to ) .get();
BSONWritable pkeyOut = new BSONWritable(outDoc); pContext.write( pkeyOut, new IntWritable(sum) ); }
Example 1 - Java MapReduce (cont)
mongo.job.input.format=com.mongodb.hadoop.MongoInputFormatmongo.input.uri=mongodb://my-db:27017/enron.messages
Read from MongoDB
Read from BSON
mongo.job.input.format=com.mongodb.hadoop.BSONFileInputFormatmapred.input.dir=file:///tmp/messages.bsonhdfs:///tmp/messages.bsons3:///tmp/messages.bson
Example 1 - Java MapReduce (cont)
mongo.job.output.format=com.mongodb.hadoop.MongoOutputFormatmongo.output.uri=mongodb://my-db:27017/enron.results_out
Write output to MongoDB
Write output to BSON
mongo.job.output.format=com.mongodb.hadoop.BSONFileOutputFormatmapred.output.dir=file:///tmp/results.bson
hdfs:///tmp/results.bsons3:///tmp/results.bson
Results : Output Data
mongos> db.results_out.find({"_id.t": /^kenneth.lay/}){ "_id" : { "t" : "[email protected]", "f" : "[email protected]" }, "count" : 1 }{ "_id" : { "t" : "[email protected]", "f" : "[email protected]" }, "count" : 1 }{ "_id" : { "t" : "[email protected]", "f" : "[email protected]" }, "count" : 2 }{ "_id" : { "t" : "[email protected]", "f" : "[email protected]" }, "count" : 2 }{ "_id" : { "t" : "[email protected]", "f" : "[email protected]" }, "count" : 4 }{ "_id" : { "t" : "[email protected]", "f" : "[email protected]" }, "count" : 1 }{ "_id" : { "t" : "[email protected]", "f" : "[email protected]" }, "count" : 1 }...has more
Example 2 - Hadoop Streaming
Let’s do the same Enron MapReduce job with Python instead of Java
$ pip install pymongo_hadoop
Example 2 - Hadoop Streaming (cont)
Hadoop passes data to an external process via STDOUT/STDIN
map(k, v)map(k, v)map(k, v)map()
JVM
STDINPython / Ruby / JS
interpreter
STDOUT
Hadoop (JVM)
def mapper(documents): . . .
Example 2 - Hadoop Streaming (cont)
from pymongo_hadoop import BSONMapper
def mapper(documents): i = 0 for doc in documents: i = i + 1 from_field = doc['headers']['From'] to_field = doc['headers']['To'] recips = [x.strip() for x in to_field.split(',')] for r in recips: yield {'_id': {'f':from_field, 't':r}, 'count': 1}
BSONMapper(mapper)print >> sys.stderr, "Done Mapping."
BSONMapper is pymongo layer that translates from hadoop streaming back to hadoop
Example 2 - Hadoop Streaming (cont)
from pymongo_hadoop import BSONReducer
def reducer(key, values): print >> sys.stderr, "Processing from/to %s" % str(key) _count = 0 for v in values: _count += v['count'] return {'_id': key, 'count': _count}
BSONReducer(reducer)
Surviving Hadoop:making MapReduce easier
Pig + Hive
writing m/r jobs from scratch can be clunky and cumbersome
Example 3 - Mongo-Hadoop and Pig
Let’s do the same thing yet again, but this time using Pig
Pig is a powerful language that can generate sophisticated MapReduce workflows from simple
scripts
Can perform JOIN, GROUP, and execute user-defined functions (UDFs)
Example 3 - Mongo-Hadoop and Pig (cont)
Pig directives for loading data:BSONLoader and MongoLoader
Writing data outBSONStorage and MongoInsertStorage
data = LOAD 'mongodb://localhost:27017/db.collection' using com.mongodb.hadoop.pig.MongoLoader;
STORE records INTO 'file:///output.bson' using com.mongodb.hadoop.pig.BSONStorage;
Pig has its own special datatypes: Bags, Maps, and Tuples
Mongo-Hadoop Connector intelligently converts between Pig datatypes and
MongoDB datatypes
Example 3 - Mongo-Hadoop and Pig (cont)
bags -> arraysmaps -> objects
raw = LOAD 'hdfs:///messages.bson' using com.mongodb.hadoop.pig.BSONLoader('','headers:[]') ;
send_recip = FOREACH raw GENERATE $0#'From' as from, $0#'To' as to;
send_recip_filtered = FILTER send_recip BY to IS NOT NULL;
send_recip_split = FOREACH send_recip_filtered GENERATE from as from, TRIM(FLATTEN(TOKENIZE(to))) as to;
send_recip_grouped = GROUP send_recip_split BY (from, to);send_recip_counted = FOREACH send_recip_grouped GENERATE group, COUNT($1) as count;
STORE send_recip_counted INTO 'file:///enron_results.bson' using com.mongodb.hadoop.pig.BSONStorage;
Example 3 - Mongo-Hadoop and Pig (cont)
Hive with Mongo-Hadoop
Similar idea to Pig - process your data without needing to write MapReduce code from
scratch
...but with SQL as the language of choice
Hive with Mongo-Hadoop
Sample Data:db.users
db.users.find(){ "_id": 1, "name": "Tom", "age": 28 }{ "_id": 2, "name": "Alice", "age": 18 }{ "_id": 3, "name": "Bob", "age": 29 }{ "_id": 101, "name": "Scott", "age": 10 }{ "_id": 104, "name": "Jesse", "age": 52 }{ "_id": 110, "name": "Mike", "age": 32 }...
CREATE TABLE mongo_users (id int, name string, age int)STORED BY "com.mongodb.hadoop.hive.MongoStorageHandler"WITH SERDEPROPERTIES( "mongo.columns.mapping" = "_id,name,age" )TBLPROPERTIES ( "mongo.uri" = "mongodb://localhost:27017/test.users");
first, declare the collection to be accessible in Hive:
Hive with Mongo-Hadoop
. . .then you can run SQL on it, like a table.SELECT name,age FROM mongo_users WHERE id > 100 ;
SELECT * FROM mongo_users GROUP BY age WHERE id > 100 ;
you can use GROUP BY:
or JOIN multiple tables/collections together:
SELECT * FROM mongo_users T1 JOIN user_emails T2 WHERE T1.id = T2.id;
subset of SQL
Write the output of queries back into new tables:
INSERT OVERWRITE TABLE old_users SELECT id,name,age FROM mongo_users WHERE age > 100 ;
DROP TABLE mongo_users;
Drop a table in Hive to delete the underlying collection in MongoDB
use “external” when declaring your table to prevent the collection drop
Usage with Amazon Elastic MapReduce
Run mongo-hadoop jobs without needing to set up or manage your
own Hadoop cluster.
Pig, Hive, and streaming work on EMR, too!Logs get captured into S3 files
Usage with Amazon Elastic MapReduce
First, make a “bootstrap” script that fetches dependencies (mongo-hadoop
jar and java drivers) #!/bin/sh
wget -P /home/hadoop/lib http://central.maven.org/maven2/org/mongodb/mongo-java-driver/2.12.2/mongo-java-driver-2.12.2.jar
wget -P /home/hadoop/lib https://s3.amazonaws.com/mongo-hadoop-code/mongo-hadoop-core_1.1.2-1.1.0.jar
this will get executed on each node in the cluster that EMR builds for us.
working on updating hadoop artifacts in maven
Example 4 - Usage with Amazon Elastic MapReduce
Put the bootstrap script, and all your code, into an S3 bucket where Amazon can see it.
s3cp ./bootstrap.sh s3://$S3_BUCKET/bootstrap.shs3mod s3://$S3_BUCKET/bootstrap.sh public-read
s3cp $HERE/../enron/target/enron-example.jar s3://$S3_BUCKET/enron-example.jars3mod s3://$S3_BUCKET/enron-example.jar public-read
$ elastic-mapreduce --create --jobflow ENRON000 --instance-type m1.xlarge --num-instances 5 --bootstrap-action s3://$S3_BUCKET/bootstrap.sh --log-uri s3://$S3_BUCKET/enron_logs --jar s3://$S3_BUCKET/enron-example.jar --arg -D --arg mongo.job.input.format=com.mongodb.hadoop.BSONFileInputFormat --arg -D --arg mapred.input.dir=s3n://mongo-test-data/messages.bson --arg -D --arg mapred.output.dir=s3n://$S3_BUCKET/BSON_OUT --arg -D --arg mongo.job.output.format=com.mongodb.hadoop.BSONFileOutputFormat # (any additional parameters here)
Example 4 - Usage with Amazon Elastic MapReduce
. . .then launch the job from the command line, pointing to your S3 locations
Control the type and number of instances
in the cluster
Example 4 - Usage with Amazon Elastic MapReduce
Easy to kick off a Hadoop job, without needing to manage a Hadoop cluster
Pig, Hive, and streaming work on EMR, too!
Logs get captured into S3 files
Example 5 - New Feature: MongoUpdateWritable
. . . but we can also modify an existing output collection
Works by applying mongodb update modifiers:$push, $pull, $addToSet, $inc, $set, etc.
Can be used to do incremental MapReduce or“join” two collections
In previous examples, we wrote job output data by inserting into a new collection
Example 5 - MongoUpdateWritableFor example,
let’s say we have two collections.
{ "_id": ObjectId("51b792d381c3e67b0a18d678"), "sensor_id": ObjectId("51b792d381c3e67b0a18d4a1"), "value": 3328.5895416489802, "timestamp": ISODate("2013-‐05-‐18T13:11:38.709-‐0400"), "loc": [-‐175.13,51.658]}
{ "_id": ObjectId("51b792d381c3e67b0a18d0ed"), "name": "730LsRkX", "type": "pressure", "owner": "steve",}
sensors
log events
refers to which sensor logged the event
For each owner, we want to calculate how many events were recorded for each type of sensor that logged it.
For each owner, we want to calculate how many events were recorded for each type of sensor that logged it.
Plain english:
Bob’s sensors for temperature have stored 1300 readingsBob’s sensors for pressure have stored 400 readings
Alice’s sensors for humidity have stored 600 readingsAlice’s sensors for temperature have stored 700 readings
etc...
sensors(mongodb collection)
Stage 1 -MapReduce on sensors collection
Results(mongodb collection)
for each sensor, emit: {key: owner+type, value: _id}
group data from map() under each key, output:{key: owner+type, val: [ list of _ids] }
read from mongodb
insert() new records to mongodb
MapReduce
log events(mongodb collection)
do this in two stages
the sensor’s owner and type
After stage one, the output docs look like:
list of ID’s of sensors with this owner and type
{ "_id": "alice pressure", "sensors": [ ObjectId("51b792d381c3e67b0a18d475"), ObjectId("51b792d381c3e67b0a18d16d"), ObjectId("51b792d381c3e67b0a18d2bf"), … ]}
Now we just need to count the total # of log events recorded for any sensors that appear in the list for each owner/type group.
sensors(mongodb collection)
Stage 2 -MapReduce on log events collection
read from mongodb
Results(mongodb collection)
update() existing records in mongodb
MapReduce
log events(mongodb collection)
for each sensor, emit: {key: sensor_id, value: 1}
group data from map() under each keyfor each value in that key: update({sensors: key}, {$inc : {logs_count:1}})
context.write(null, new MongoUpdateWritable( query, //which documents to modify update, //how to modify ($inc) true, //upsert false)); // multi
Example - MongoUpdateWritable
Result after stage 2
{ "_id": "1UoTcvnCTz temp", "sensors": [ ObjectId("51b792d381c3e67b0a18d475"), ObjectId("51b792d381c3e67b0a18d16d"), ObjectId("51b792d381c3e67b0a18d2bf"), … ], "logs_count": 1050616}
now populated with correct count
New Features in v1.2 and beyond
Continually improving Hive support
Performance Improvements - Lazy BSON
Support for multi-collection input sources
API for adding custom splitter implementations
and more
primarily focusing on hive but pig is nextmaven central
Recap
Mongo-Hadoop - use Hadoop to do massive computations on big data sets stored in MongoDB/BSON
Tools and APIs make it easier: Streaming, Pig, Hive, EMR, etc.
MongoDB becomes a Hadoop-enabled filesystem
Questions?
https://github.com/mongodb/mongo-hadoop/tree/master/examples
Examples can be found on github:
MongoDB WorldNew York City, June 23-25
Save 25% with 25JustinLee
Register at world.mongodb.com