I am working on BI process that will read data from cassandra, create summaries using Map Reduce and write back to a different keyspace.
Starting with a single node, everything worked as i expected, but when moving to a multi-node, i am not sure I fully understand the topology and configuration.
I have a setup with 3 nodes. Each has a Cassandra node (version 1.1.9), data node and task tracker (version 0.20.2+923.421- CDH3U5) . The NameNode and job tracker are on a different server. At this point i am trying to run Pig script from the DataNode server.
The thing i am not sure of is the pig argument PIG_INITIAL_ADDRESS. I assumed the query would run on all Cassandra nodes, each task tracker would only query the local Cassandra node, and the reducer would handle any duplicates. Based on that assumption i thought the PIG_INITIAL_ADDRESS should be localhost. But when running the pig script it fails:
java.io.IOException: Unable to connect to server localhost:9160
My questions are- should the initial address be any one of the Cassandra nodes, and Splitting the map on the cluster is done from Cassandra keys partitions (will i get the distribution i need)?
IF I where to use java map reduce, will i still need to supply the initial address?
Is the current implementation assumes pig is running from a Cassandra node?
The PIG_INITIAL_ADDRESS is the address of one of the Cassandra nodes in your ring. In order to have the Hadoop job read data from or write data to Cassandra, it just needs to have some properties set. Those properties are also available to set in the job properties or in the default Hadoop configuration on the server that you're running the job from. Other than that, it's just like submitting a job to a job tracker.
For more information, I would look at the readme that's in the cassandra source download under examples/pig. There is a lot of explanation in there as well.
Related
I have read documents where it is recommended to install sqoop on edgenode for many reasons which is understood and for every mapper a connection to source database is established. My question is will all the 4 connections be established from edgenode or sqoop-client in edgenode just creates some kind of driver which monitors the ingestion while datanodes connect to the databases,get the data(part) and split it locally and then put in HDFS.
Sqoop is a wrapper over Map reduce to perform import export operation.
Mappers will run in your cluster , while the sqoop client will run the edge node.
Each mapper will open a connection to your database.
What rows are consumed by your mapper are decided by the client when submitting the job.
Edge node acts as an interface to Hadoop cluster, sqoop import/export launches the MapReduce job based on the generic and specific arguments.
MapReduce job runs the number of mappers based on the -m or --num-mappers argument given.
For Detailed information see below links:
http://www.dummies.com/programming/big-data/hadoop/edge-nodes-in-hadoop-clusters/
https://sqoop.apache.org/docs/1.4.0-incubating/SqoopUserGuide.html#id1764013
I understand the concepts of HDFS and Map Reduce and how it is important to move the processing logic to the data to increase efficiency. I was even able to run a couple of map reduce job on my basic Hadoop cluster. Surrounding these concepts there are a lot of different technologies like YARN, HUE, OOZIE all of which seems to do the same thing (at least from a very high level) which is operation visibility and CRUD abilities for jobs (which can be map-reduce or something else).
Am I correct in making this assumption or is there a much more fundamental difference between them?
Thanks
Kay
YARN - Map Reduce is API where you have to implement data processing logic in it. Once the code is compiled you have to submit the jobs using hadoop jar command. YARN is the framework which will keep track of the resources, submit the job on the cluster, execute the job, show/log the progress.
OOZIE - Take a data integration example. You might have to get a data set from one database and other data set from other database, then you want to join, process the data and reload it into a cache or 3rd database. It involves 2 sqoop jobs to pull data from database, a hive/map reduce job to join and process the data, then push into cache/database. All these jobs are dependent on each other, eg: we are supposed to process the data only after data is pulled from source databases. Hence we need to create a workflow to execute complete data integration process. OOZIE can facilitate that. It is map reduce based workflow tool. Workflow it self will be executed as one or more map reduce jobs.
HUE: There are many tools in Hadoop - HDFS (file system), Sqoop, Hive/pig to process the data, Impala, HBase and many many more. To execute the POCs, it can get tedious to connect to the cluster. Also it need some linux skills. To overcome those challenges all the Hadoop eco system tools are consolidate under one umbrella - called Hue.
I am building a new Hadoop cluster (expanding number of nodes and extending capacity of current nodes) and need to back up all of the existing data. Right now I am just tar-ing everything and sending it to another server.
Is there a smarter way of doing this which will allow me to easily deploy once the new cluster is set up?
Edit: I should also point out that I don't store any data on the cluster. I bring data to the cluster, process it, and then send the processed data back to the original server. Any temporary data on the cluster is the deleted.
Use Distcp to transfer the HDFS data to other cluster or any cloud inorder to store the data.
If you want to schedule the Backup process you may avail OOZIE-DISTCP for backup process!!
I need to execute MapReduce on my Cassandra cluster, including data locality, ie. each job queries only rows which belong to local Casandra Node where the job runs.
Tutorials exist, on how to setup Hadoop for MR on older Cassandra version (0.7). I cannot find such for current release.
What has changed since 0.7 in this regard ?
What software modules are required for minimal setup (Hadoop+HDFS+...)?
Do I need Cassandra Enterprise ?
Cassandra contains a few classes which are sufficient to integrate with Hadoop:
ColumnFamilyInputFormat - This is an input for a Map function which can read all rows from a single CF in when using Cassandra's random partitioner, or it can read a row range when used with Cassandra's ordered partitioner. Cassandra cluster has ring form, where each ring part is responsible for concrete key range. Main task of Input Format is to divide Map input into data parts which can be processed in parallel - those are called InputSplits. In Cassandra case this is simple - each ring range has one master node, and this means that Input Format will create one InputSplit for each ring element, and it will result in one Map task. Now we would like to execute our Map task on the same host where data is stored. Each InputSplit remembers IP address of its ring part - this is the IP address of Cassandra node responsible to this particular key range. JobTracker will create Map tasks form InputSplits and assign them to TaskTracker for execution. JobTracker will try to find TaskTracker which has the same IP address as InputSplit - basically we have to start TaskTracker on Cassandra host, and this will guarantee data locality.
ColumnFamilyOutputFormat - this configures context for Reduce function. So that the results can be stored in Cassandra
Results from all Map functions has to be combined together before they can be passed to reduce function - this is called shuffle. It uses local file system - from Cassandra perspective nothing has to be done here, we just need to configure path to local temp directory. Also there is no need to replace this solution with something else (like persisting in Cassandra) - this data does not have to be replicated, Map tasks are idempotent.
Basically using provided Hadoop integration gives up possibility to execute Map job on hosts where data resides, and Reduce function can store results back into Cassandra - it's all that I need.
There are two possibilities to execute Map-Reduce:
org.apache.hadoop.mapreduce.Job - this class simulates Hadoop in one process. It executes Map-Resuce task and does not require any additional services/dependencies, it needs only access to temp directory to store results from map job for shuffle. Basically we have to call few setters on Job class, which contain things like class names for Map task, Reduce task, input format, Cassandra connection, when setup is done job.waitForCompletion(true) has to be called - it starts Map-Reduce task and waits for results. This solution can be used to quickly get into Hadoop world, and for testing. It will not scale (single process), and it will fetch data over network, but still - it will be fine for beginning.
Real Hadoop cluster - I did not set it up yet, but as I understood, Map-Reduce jobs from previous example will work just fine. We need additionally HDFS which will be used to distribute jars containing Map-Reduce classes in Hadoop cluster.
yes I was looking for the same thing, seems DataStaxEnterprise has a simplified Hadoop integration,
read this http://wiki.apache.org/cassandra/HadoopSupport
I am new in hadoop so I have some doubts. If the master-node fails what happened the hadoop cluster? Can we recover that node without any loss? Is it possible to keep a secondary master-node to switch automatically to the master when the current one fails?
We have the backup of the namenode (Secondary namenode), so we can restore the namenode from Secondary namenode when it fails. Like this, How can we restore the data's in datanode when the datanode fails? The secondary namenode is the backup of namenode only not to datenode, right? If a node is failed before completion of a job, so there is job pending in job tracker, is that job continue or restart from the first in the free node?
How can we restore the entire cluster data if anything happens?
And my final question, can we use C program in Mapreduce (For example, Bubble sort in mapreduce)?
Thanks in advance
Although, It is too late to answer your question but just It may help others..
First of all let me Introduce you with Secondary Name Node:
It Contains the name space image, edit log files' back up for past one
hour (configurable). And its work is to merge latest Name Node
NameSpaceImage and edit logs files to upload back to Name Node as
replacement of the old one. To have a Secondary NN in a cluster is not
mandatory.
Now coming to your concerns..
If the master-node fails what happened the hadoop cluster?
Supporting Frail's answer, Yes hadoop has single point of failure so
whole of your currently running task like Map-Reduce or any other that
is using the failed master node will stop. The whole cluster including
client will stop working.
Can we recover that node without any loss?
That is hypothetical, Without loss it is least possible, as all the
data (block reports) will lost which has sent by Data nodes to Name
node after last back up taken by secondary name node. Why I mentioned
least, because If name node fails just after a successful back up run
by secondary name node then it is in safe state.
Is it possible to keep a secondary master-node to switch automatically to the master when the current one fails?
It is staright possible by an Administrator (User). And to switch it
automatically you have to write a native code out of the cluster, Code
to moniter the cluster that will cofigure the secondary name node
smartly and restart the cluster with new name node address.
We have the backup of the namenode (Secondary namenode), so we can restore the namenode from Secondary namenode when it fails. Like this, How can we restore the data's in datanode when the datanode fails?
It is about replication factor, We have 3 (default as best practice,
configurable) replicas of each file block all in different data nodes.
So in case of failure for time being we have 2 back up data nodes.
Later Name node will create one more replica of the data that failed
data node contained.
The secondary namenode is the backup of namenode only not to datenode, right?
Right. It just contains all the metadata of data nodes like data node
address,properties including block report of each data node.
If a node is failed before completion of a job, so there is job pending in job tracker, is that job continue or restart from the first in the free node?
HDFS will forcely try to continue the job. But again it depends on
replication factor, rack awareness and other configuration made by
admin. But if following Hadoop's best practices about HDFS then it
will not get failed. JobTracker will get replicated node address to
continnue.
How can we restore the entire cluster data if anything happens?
By Restarting it.
And my final question, can we use C program in Mapreduce (For example, Bubble sort in mapreduce)?
yes, you can use any programming language which support Standard file
read write operations.
I Just gave a try. Hope it will help you as well as others.
*Suggestions/Improvements are welcome.*
Currently hadoop cluster has a single point of failure which is namenode.
And about the secondary node isssue (from apache wiki) :
The term "secondary name-node" is somewhat misleading. It is not a
name-node in the sense that data-nodes cannot connect to the secondary
name-node, and in no event it can replace the primary name-node in
case of its failure.
The only purpose of the secondary name-node is to perform periodic
checkpoints. The secondary name-node periodically downloads current
name-node image and edits log files, joins them into new image and
uploads the new image back to the (primary and the only) name-node.
See User Guide.
So if the name-node fails and you can restart it on the same physical
node then there is no need to shutdown data-nodes, just the name-node
need to be restarted. If you cannot use the old node anymore you will
need to copy the latest image somewhere else. The latest image can be
found either on the node that used to be the primary before failure if
available; or on the secondary name-node. The latter will be the
latest checkpoint without subsequent edits logs, that is the most
recent name space modifications may be missing there. You will also
need to restart the whole cluster in this case.
There are tricky ways to overcome this single point of failure. If you are using cloudera distribution, one of the ways explained here. Mapr distribution has a different way to handle to this spof.
Finally, you can use every single programing language to write map reduce over hadoop streaming.
Although, It is too late to answer your question but just It may help others..firstly we will discuss role of Hadoop 1.X daemons and then your issues..
1. What is role of secondary name Node
it is not exactly a backup node. it reads a edit logs and create updated fsimage file for name node periodically. it get metadata from name node periodically and keep it and uses when name node fails.
2. what is role of name node
it is manager of all daemons. its master jvm proceess which run at master node. it interact with data nodes.
3. what is role of job tracker
it accepts job and distributes to task trackers for processing at data nodes. its called as map process
4. what is role of task trackers
it will execute program provided for processing on existing data at data node. that process is called as map.
limitations of hadoop 1.X
single point of failure
which is name node so we can maintain high quality hardware for the name node. if name node fails everything will be inaccessible
Solutions
solution to single point of failure is hadoop 2.X which provides high availability.
high availability with hadoop 2.X
now your topics ....
How can we restore the entire cluster data if anything happens?
if cluster fails we can restart it..
If a node is failed before completion of a job, so there is job pending in job tracker, is that job continue or restart from the first in the free node?
we have default 3 replicas of data(i mean blocks) to get high availability it depends upon admin that how much replicas he has set...so job trackers will continue with other copy of data on other data node
can we use C program in Mapreduce (For example, Bubble sort in mapreduce)?
basically mapreduce is execution engine which will solve or process big data problem in(storage plus processing) distributed manners. we are doing file handling and all other basic operations using mapreduce programming so we can use any language of where we can handle files as per the requirements.
hadoop 1.X architecture
hadoop 1.x has 4 basic daemons
I Just gave a try. Hope it will help you as well as others.
Suggestions/Improvements are welcome.