practical hadoop using pig
DESCRIPTION
So you want to get started with Hadoop, but how. This session will show you how to get started with Hadoop development using Pig. Prior Hadoop experience is not needed. Thursday, May 8th, 02:00pm-02:50pmTRANSCRIPT
Practical Hadoop with Pig
Dave Wellman
#openwest @dwellman
How does it all work?
HDFSHadoop Shell
MR Data Structures Pig Commands
Pig Example
HDFS
HDFS has 3 main actors
The Name Node
The Name Node is “The Conductor”.
It directs the performance of the cluster.
The Data Nodes:
A Data Node stores blocks of data.
Clusters can be contain thousands of Data Nodes.
*Yahoo has a 40,000 node cluster.
The Client
The client is a window to the cluster.
The Name Node
The heart of the System.
The heart of the System.
Maintains a virtual File Directory.
The heart of the System.
Maintains a virtual File Directory.
Tracks all the nodes.
The heart of the System.
Maintains a virtual File Directory.
Tracks all the nodes.
Listens for “heartbeats” and “Block Reports” (more on this later).
The heart of the System.
Maintains a virtual File Directory.
Tracks all the nodes.
Listens for “heartbeats” and “Block Reports” (more on this later).
If the NameNode is down, the cluster is offline.
Storing Data
The Data Nodes
Add a Data Node:
Add a Data Node:
The Data Node says “Hello” to the Name Node.
Add a Data Node:
The Data Node says “Hello” to the Name Node.
The Name Node offers the Data Node a handshake with version requirements.
Add a Data Node:
The Data Node says “Hello” to the Name Node.
The Name Node offers the Data Node a handshake with version requirements.
The Data Node replies back to the Name Node, “Okay”, or “Shuts Down”.
Add a Data Node:
The Data Node says “Hello” to the Name Node.
The Name Node offers the Data Node a handshake with version requirements.
The Data Node replies back to the Name Node, “Okay”, or “Shuts Down”.
The Name Node hands the Data Node a NodeId that it remembers.
.
Add a Data Node:
The Data Node says “Hello” to the Name Node.
The Name Node offers the Data Node a handshake with version requirements.
The Data Node replies back to the Name Node, “Okay”, or “Shuts Down”.
The Name Node hands the Data Node a NodeId that it remembers.
The Data Node is now part of cluster and it checks in with the Name Node every 3 seconds.
Data Node Heartbeat:
Data Node Heartbeat:
The “check-in” is a simple HTTP Request/Response.
Data Node Heartbeat:
The “check-in” is a simple HTTP Request/Response.
This "check-in" is very important communication protocol that guarantees the health of the cluster.
Data Node Heartbeat:
The “check-in” is a simple HTTP Request/Response.
This "check-in" is very important communication protocol that guarantees the health of the cluster.
Block Reports – what data I have and is it okay.
Data Node Heartbeat:
The “check-in” is a simple HTTP Request/Response.
This "check-in" is very important communication protocol that guarantees the health of the cluster.
Block Reports – what data I have and is it okay.
Name Node controls the Data Nodes by issuing orders when they return and report their status.
Data Node Heartbeat:
The “check-in” is a simple HTTP Request/Response.
This "check-in" is very important communication protocol that guarantees the health of the cluster.
Block Reports – what data I have and is it okay.
Name Node controls the Data Nodes by issuing orders when they return and report their status.
Replicate Data, Delete Data, Verify Data
Data Node Heartbeat:
The “check-in” is a simple HTTP Request/Response.
This "check-in" is very important communication protocol that guarantees the health of the cluster.
Block Reports – what data I have and is it okay.
Name Node controls the Data Nodes by issuing orders when they return and report their status.
Replicate Data, Delete Data, Verify Data
Same process for all nodes within a cluster.
Writing Data
The client “tells” the NameNode the virtual directory location for the file.
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The client “tells” the NameNode the virtual directory location for the file.
The Client breaks the file into 64MB “blocks”
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The client “tells” the NameNode the virtual directory location for the file.
The Client breaks the file into 64MB “blocks”
The client “ask” the NameNode where the blocks go.
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The client “tells” the NameNode the virtual directory location for the file.
The Client breaks the file into 64MB “blocks”
The client “ask” the NameNode where the blocks go.
The Client “stream” the blocks, in parallel, to the DataNodes.
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The client “tells” the NameNode the virtual directory location for the file.
The Client breaks the file into 64MB “blocks”
The client “ask” the NameNode where the blocks go.
The Client “stream” the blocks, in parallel, to the DataNodes.
DataNode(s) tells the NameNode they have the data via the block report
The client “tells” the NameNode the virtual directory location for the file.
The Client breaks the file into 64MB “blocks”
The client “ask” the NameNode where the blocks go.
The Client “stream” the blocks, in parallel, to the DataNodes.
DataNode(s) tells the NameNode they have the data via the block report
The NameNode tells the DataNode where to replicate the block.
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Reading Data
The client tells the NameNode it would like to read a file.
The client tells the NameNode it would like to read a file.
The NameNode reply’s with the list of blocks and the nodes the blocks are on.
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The client tells the NameNode it would like to read a file.
The NameNode reply’s with the list of blocks and the nodes the blocks are on.
The client request the first block from a DataNode
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A64The client tells the NameNode it would like to read a file.
The NameNode reply’s with the list of blocks and the nodes the blocks are on.
The client request the first block from a DataNode
The client compares the checksum of the block against the manifest from the NameNode.
The client tells the NameNode it would like to read a file.
The NameNode reply’s with the list of blocks and the nodes the blocks are on.
The client request the first block from a DataNode
The client compares the checksum of the block against the manifest from the NameNode.
The client moves on to the next block in the sequence until the file has been read.
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Failure Recovery
A Data Node Fails to “check-in”
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A Data Node Fails to “check-in”
After 10 minutes the Name Node gives up on that Data Node.
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A Data Node Fails to “check-in”
After 10 minutes the Name Node gives up on that Data Node.
When another node that has blocks originally assigned to the lost node checks-in, the name node sends a block replication command. A64XA64
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A Data Node Fails to “check-in”
After 10 minutes the Name Node gives up on that Data Node.
When another node that has blocks originally assigned to the lost node checks-in, the name node sends a block replication command.
The Data Node replicates that block of data. (Just like a write)
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Interacting with HadoopHDFS Shell Commands
HDFS Shell Commands.
> Hadoop fs –ls <args>
Same as unix or osx ls command.
/user/hadoop/file1 /user/hadoop/file2...
HDFS Shell Commands.
> Hadoop fs –mkdir <path>
Creates directories in HDFS using path.
HDFS Shell Commands.
> hadoop fs -copyFromLocal <localsrc> URI
Copy a file from your client to HDFS.
Similar to put command, except that the source is restricted to a local file reference.
HDFS Shell Commands.
> hadoop fs -cat <path>
Copies source paths to stdout.
HDFS Shell Commands.
> hadoop fs -copyToLocal URI <localdst>
Copy a file from HDFS to your client.
Similar to get command, except that the destination is restricted to a local file reference.
HDFS Shell Commands.
catchgrpchmodchowncopyFromLocalcopyToLocalcpdudusexpungegetgetmergels
lsrmkdirmovefromLocalmvputrmrmrsetrepstattailtesttexttouchz
Map Reduce Data StructuresBasic, Tuples & Bags
Basic Data Types:
Strings, Integers, Doubles, Longs, Byte,
Boolean, etc.
Advanced Data Types:
Tuples and Bags
Tuples are JSON like and simple.
raw_data: {date_time: bytearray,seconds: bytearray
}
Bags hold Tuples and Bags
element: {date_time: bytearray,seconds: bytearraygroup: chararray,ordered_list: {
date: chararray,hour: chararray,score: long
}}
Expert Advice:
Always know your data structures.
They are the foundation for all Map Reduce
operations.
Complex (deep) data structures will kill -9
performance.
Keep them simple!
Processing DataInteracting with Pig using Grunt
GRUNT
Grunt is a command line interface used to
debug pig jobs. Similar to Ruby IRB or
Groovy CLI.
Grunt is your best weapon against bad
pigs.
pig -x local
Grunt> |
GRUNT
Grunt> describe Element
Describe will display the data structure of
an Element
Grunt> dump Element
Dump will display the data represented by
an Element
GRUNT
> describe raw_data
Produces the output:> raw_data: { date_time: bytearray, items: bytearray }
Or in a more human readable form:Raw_data: {
date_time: bytearray,items: bytearray
}
GRUNT
> dump raw_data
You can dump terabytes of data to your screen,
so be careful.
(05/10/2011 20:30:00.0,0)(05/10/2011 20:45:00.0,0)(05/10/2011 21:00:00.0,0)(05/10/2011 21:15:00.0,0)...
Pig ProgramsMap Reduce Made Simple
Most PIG commands are assignments.
• The element names the collection of records that exist out in the cluster.
• It’s not a traditional programming variable. • It describes the data from the operation.• It does not change.
Element = Operation;
The SET command
Used to set a hadoop job variable. Like the name of
your pig job.
SET job.name 'Day over Day - [$input]’;
The REGISTER and DEFINE commands
-- Setup udf jarsREGISTER $jar_prefix/sidekick-hadoop-0.0.1.jar
DEFINE BUCKET_FORMAT_DATE com.sidekick.hadoop.udf.UnixTimeFormatter('MM/dd/yyyy HH:mm', 'HH');
The LOAD USING command
-- load in the data from HDFS
raw_data = LOAD '$input' USING PigStorage('\t') AS (date_time, items);
The FILTER BY command
Selects tuples from a relation based on some condition.
-- filter to the week we wantbroadcast_week = FILTER bucket_list BY (date >= '03-Oct-2011') AND (date <= '10-Oct-2011');
The GROUP BY command
Groups the data in one or multiple relations.
daily_stats = GROUP broadcast_week BY (date,
hour);
The FOREACH command
Generates data transformations based on columns of
data.
bucket_list = FOREACH raw_data GENERATE FLATTEN(DATE_FORMAT_DATE(date_time)) AS
date,MINUTE_BUCKET(date_time) AS hour, MAX_ITEMS(items) AS items;
*DATE_FORMAT_DATE is a user defined function, an advanced topic we’ll come to in a minute.
The GENERATE command
Use the FOREACH GENERATE operation to work with
columns of data.
bucket_list = FOREACH raw_data GENERATE FLATTEN(DATE_FORMAT_DATE(date_time)) AS
date,MINUTE_BUCKET(date_time) AS hour, MAX_ITEMSS(items) AS items;
The FLATTEN command
FLATTEN substitutes the fields of a tuple in place of the
tuple.
traffic_stats = FOREACH daily_stats GENERATE FLATTEN(GROUP),COUNT(broadcast_week) AS cnt,SUM(broadcast_week.items) AS total;
The STORE INTO USING command
Store function determine how data stored after a pig
job.
-- All done, now store it STORE final_results INTO '$output' USING PigStorage();
Demo Time! “Because, it’s all a big lie
until someone demos’ the code.”- Genghis Khan
Thank You.
- Genghis Khan