Which operating
system(s) are supported for production Hadoop deployment?
The main supported operating system is Linux. However, with
some additional software Hadoop can be deployed on Windows.
What is the role of
the namenode?
The namenode is the "brain" of the Hadoop cluster
and responsible for managing the distribution blocks on the system based on the
replication policy. The namenode also supplies the specific addresses for the
data based on the client requests.
What happen on the
namenode when a client tries to read a data file?
The namenode will look up the information about file in the
edit file and then retrieve the remaining information from filesystem memory
snapshot
Since the namenode needs to support a large number of the
clients, the primary namenode will only send information back for the data
location. The datanode itselt is responsible for the retrieval.
What are the hardware
requirements for a Hadoop cluster (primary and secondary namenodes and
datanodes)?
There are no requirements for
datanodes. However, the namenodes require a specified amount of RAM to store
filesystem image in memory
Based on the design of the primary
namenode and secondary namenode, entire filesystem information will be stored
in memory. Therefore, both namenodes need to have enough memory to contain the
entire filesystem image.
What mode(s) can
Hadoop code be run in?
Hadoop can be deployed in stand
alone mode, pseudo-distributed mode or fully-distributed mode. –
Hadoop was
specifically designed to be deployed on multi-node cluster. However, it also
can be deployed on single machine and as a single process for testing purposes
How would an Hadoop
administrator deploy various components of Hadoop in production?
Deploy namenode and jobtracker on
the master node, and deploy datanodes and taskstrackers on multiple slave nodes
There is a need for only one
namenode and jobtracker on the system. The number of datanodes depends on the
available hardware
What is the best
practice to deploy the secondary namenode
Deploy secondary namenode on a
separate standalone machine
The secondary namenode needs to be
deployed on a separate machine. It will not interfere with primary namenode
operations in this way. The secondary namenode must have the same memory
requirements as the main namenode.
Is there a standard
procedure to deploy Hadoop?
. No, there are some differences between various
distributions. However, they all require that Hadoop jars be installed on the
machine
There are some common requirements for all Hadoop
distributions but the specific procedures will be different for different
vendors since they all have some degree of proprietary software
What is the role of
the secondary namenode?
Secondary namenode performs CPU intensive operation of
combining edit logs and current filesystem snapshots
The secondary namenode was separated out as a process due to
having CPU intensive operations and additional requirements for metadata
back-up
What are the side
effects of not running a secondary name node?
The cluster performance will degrade over time since edit
log will grow bigger and bigger
If the secondary
namenode is not running at all, the edit log will grow significantly and it
will slow the system down. Also, the system will go into safemode for an
extended time since the namenode needs to combine the edit log and the current
filesystem checkpoint image.
What happen if a datanode loses network connection for a few minutes?
The namenode will detect that a datanode is
not responsive and will start replication of the data from remaining replicas.
When datanode comes back online, the extra replicas will be
The replication factor is actively maintained
by the namenode. The namenode monitors the status of all datanodes and keeps
track which blocks are located on that node. The moment the datanode is not
avaialble it will trigger replication of the data from the existing replicas.
However, if the datanode comes back up, overreplicated data will be deleted.
Note: the data might be deleted from the original datanode.
What happen if one of
the datanodes has much slower CPU?
The task execution will be as fast
as the slowest worker. However, if speculative execution is enabled, the
slowest worker will not have such big impact
Hadoop was specifically designed to work with commodity
hardware. The speculative execution helps to offset the slow workers. The
multiple instances of the same task will be created and job tracker will take
the first result into consideration and the second instance of the task will be
killed.
What is speculative
execution?
If speculative execution is enabled, the job tracker will
issue multiple instances of the same task on multiple nodes and it will take the
result of the task that finished first. The other instances of the task will
be killed.
|
The speculative execution is used to offset the impact of
the slow workers in the cluster. The jobtracker creates multiple instances of
the same task and takes the result of the first successful task. The rest of
the tasks will be discarded.
What is speculative
execution?
. If speculative execution is enabled, the job
tracker will issue multiple instances of the same task on multiple nodes and
it will take the result of the task that finished first. The other instances
of the task will be killed.
|
The speculative execution is used to offset the impact of
the slow workers in the cluster. The jobtracker creates multiple instances of
the same task and takes the result of the first successful task. The rest of
the tasks will be discarded.
How many racks do you
need to create an Hadoop cluster in order to make sure that the cluster
operates reliably?
In order to ensure a reliable
operation it is recommended to have at least 2 racks with rack placement
configured
Hadoop has a built-in rack awareness mechanism that allows
data distribution between different racks based on the configuration.
Are there any special
requirements for namenode?
Yes, the namenode holds
information about all files in the system and needs to be extra reliable
- The namenode is a single point of failure. It needs to be
extra reliable and metadata need to be replicated in multiple places. Note that
the community is working on solving the single point of failure issue with the
namenode.
If you have a file
128M size and replication factor is set to 3, how many blocks can you find on
the cluster that will correspond to that file (assuming the default apache and
cloudera configuration)?
. 6
Based on the configuration settings the file will be divided
into multiple blocks according to the default block size of 64M. 128M / 64M = 2
. Each block will be replicated according to replication factor settings
(default 3). 2 * 3 = 6 .
What is distributed
copy (distcp)?
Distcp is a Hadoop utility for
launching MapReduce jobs to copy data. The primary usage is for copying a large
amount of data
One of the major challenges in the Hadoop enviroment is
copying data across multiple clusters and distcp will allow multiple datanodes
to be leveraged for parallel copying of the data.
What is replication
factor?
Replication factor controls how
many times each individual block can be replicated –
Data is replicated in the Hadoop cluster based on the
replication factor. The high replication factor guarantees data availability in
the event of failure.
What daemons run on
Master nodes?
NameNode, Secondary NameNode and JobTracker
Hadoop is comprised of five separate daemons and each of
these daemon run in its own JVM. NameNode, Secondary NameNode and JobTracker
run on Master nodes. DataNode and TaskTracker run on each Slave nodes.
What is rack
awareness?
Rack awareness is the way in which
the namenode decides how to place blocks based on the rack definitions
Hadoop will try to
minimize the network traffic between datanodes within the same rack and will
only contact remote racks if it has to. The namenode is able to control this
due to rack awareness
What is the role of
the jobtracker in an Hadoop cluster? –
The jobtracker is responsible for
scheduling tasks on slave nodes, collecting results, retrying failed tasks
The job tracker is the main component of the map-reduce
execution. It control the division of the job into smaller tasks, submits tasks
to individual tasktracker, tracks the progress of the jobs and reports results
back to calling code. .
How does the Hadoop
cluster tolerate datanode failures?
Since Hadoop is design to run on commodity hardware, the
datanode failures are expected. Namenode keeps track of all available
datanodes and actively maintains replication factor on all data.
|
The namenode actively tracks the status of all datanodes and
acts immediately if the datanodes become non-responsive. The namenode is the
central "brain" of the HDFS and starts replication of the data the
moment a disconnect is detected.
What is the procedure
for namenode recovery?
A namenode can be recovered in two
ways: starting new namenode from backup metadata or promoting secondary
namenode to primary namenode
The namenode recovery procedure is very important to ensure
the reliability of the data.It can be accomplished by starting a new namenode
using backup data or by promoting the secondary namenode to primary.
Web-UI shows that
half of the datanodes are in decommissioning mode. What does that mean? Is it
safe to remove those nodes from the network?
This means that namenode is trying
retrieve data from those datanodes by moving replicas to remaining datanodes.
There is a possibility that data can be lost if administrator removes those
datanodes before decomissioning finished .
Due to replication strategy it is possible to lose some data
due to datanodes removal en masse prior to completing the decommissioning
process. Decommissioning refers to namenode trying to retrieve data from
datanodes by moving replicas to remaining datanodes
What does the Hadoop
administrator have to do after adding new datanodes to the Hadoop cluster?
Since the new nodes will not have
any data on them, the administrator needs to start the balancer to redistribute
data evenly between all nodes.
Hadoop cluster will detect new datanodes automatically.
However, in order to optimize the cluster performance it is recommended to
start rebalancer to redistribute the data between datanodes evenly.
If the Hadoop
administrator needs to make a change, which configuration file does he need to
change?
- It depends on the nature of the change. Each node has it`s own set of configuration files and they are not always the same on each node
Correct Answer is A - Each node in the Hadoop cluster has
its own configuration files and the changes needs to be made in every file. One
of the reasons for this is that configuration can be different for every node.
Map Reduce jobs are
failing on a cluster that was just restarted. They worked before restart. What
could be wrong?
The cluster is in a safe mode. The
administrator needs to wait for namenode to exit the safe mode before
restarting the jobs again
This is a very common mistake by Hadoop administrators when
there is no secondary namenode on the cluster and the cluster has not been
restarted in a long time. The namenode will go into safemode and combine the
edit log and current file system timestamp
Map Reduce jobs take
too long. What can be done to improve the performance of the cluster?
One the most common reasons for
performance problems on Hadoop cluster is uneven distribution of the tasks. The
number tasks has to match the number of available slots on the cluster
Hadoop is not a hardware aware system. It is the
responsibility of the developers and the administrators to make sure that the
resource supply and demand match.
How often do you need
to reformat the namenode?
Never. The namenode needs to
formatted only once in the beginning. Reformatting of the namenode will lead to
lost of the data on entire
The namenode is the only system that needs to be formatted
only once. It will create the directory structure for file system metadata and
create namespaceID for the entire file system. –
After increasing the
replication level, I still see that data is under replicated. What could be
wrong?
Data replication takes time due to
large quantities of data. The Hadoop administrator should allow sufficient time
for data replication
Depending on the data
size the data replication will take some time. Hadoop cluster still needs to
copy data around and if data size is big enough it is not uncommon that
replication will take from a few minutes to a few hours.
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