Storage | HDFS

The Hadoop distributed file system (HDFS) is a distributed, scalable, and portable file system written in Java for the Hadoop framework. Some consider it to instead be a data store due to its lack of POSIX compliance, but it does provide shell commands and Java application programming interface (API) methods that are similar to other filesystems.1
The Netdata Agent monitors the number of failed volumes in your HDFS cluster. Receiving this alert (in warning state) indicates that at least one volume has failed due to a hardware failure or misconfiguration, for example duplicate mount.

In an HDFS cluster, a single volume failing on a DataNode will cause the entire node to go offline ( to be more precise it depends on the dfs.datanode.failed.volumes.tolerated setting for your cluster). In case of a volume fail incident, the NameNode must copy any under-replicated blocks that were lost on that node, causing a burst in network traffic and potential performance degradation.

See more about Hadoop

A great explanation of the HDFS is presented in wikipedia 1 , here you can see some highlight points

HDFS provides a software framework for distributed storage and processing of big data using theMapReduce programming model. HDFS is used for storing the data and MapReduce is used for processing data. It achieves reliability by replicating the data across multiple hosts, and hence theoretically does not require redundant array of independent disks (RAID) storage on hosts. With the default replication value, 3, data is stored on three nodes, two on the same rack, and one on a different rack. DataNodes can talk to each other to rebalance data, to move copies around, and to keep the replication of data high.

HDFS has five services as follows:

  1. Name Node
  2. Secondary Name Node
  3. Job tracker
  4. Data Node
  5. Task Tracker

Top three are Master Services/Daemons/Nodes and bottom two are Slave Services. Master Services can communicate with each other and in the same way Slave services can communicate with each other. NameNode is a master node and DataNode(s) is its corresponding Slave(s) node(s) and can talk with each other.

  • NameNode: HDFS consists of only one NameNode that is called the Master node. The master node can track files, manage the file system and has the metadata of all the stored data within it. Some of the information the NameNode keeps track of are:

    • the details (metadata) of blocks
    • in which DataNode each block lives, and its location
    • replication metadata of each block

    The NameNode is the gateway that a client uses to manage the HDFS cluster.

  • DataNode: A DataNode stores data in it as blocks. This is also known as the slave node and it
    stores the actual data into HDFS which is responsible for the client to read and write. These are
    slave daemons. Every DataNode sends a Heartbeat message to the NameNode every 3 seconds and
    conveys that it is alive. In this way when NameNode does not receive a heartbeat from
    a DataNode for 2 minutes, it will take that DataNode as dead and starts the process of block
    replications on some other DataNode.

  • Secondary NameNode: This is only to take care of the checkpoints of the file system metadata which
    is in the NameNode. This is also known as the checkpoint node. It is the helper node for the
    NameNode. The secondary NameNode instructs the NameNode to create and send an fsimage and
    editlog file. The secondary NameNode create a compacted fsimage file using these inputs.

  • Job Tracker: Job Tracker receives the requests for MapReduce execution from the client. Job
    tracker talks to the NameNode to know about the location of the data that will be used in
    processing. The NameNode responds with the metadata of the required processing data.

  • Task Tracker: It is the Slave node for the Job Tracker and, it will take the task from the Job
    Tracker. It also receives code from the Job Tracker. Task Tracker will take the code and apply on
    the file. The process of applying that code on the file is known as Mapper

Some more useful information/concepts about HDFS from the official
website 2 :

  • The File System Namespace: HDFS supports a traditional hierarchical file organization. A user or
    an application can create directories and store files inside these directories. The file system
    namespace hierarchy is similar to most other existing file systems; one can create and remove
    files, move a file from one directory to another, or rename a file. HDFS does not yet implement
    user quotas. HDFS does not support hard links or soft links. However, the HDFS architecture does
    not preclude implementing these features.

    The NameNode maintains the file system namespace. Any change to the file system namespace or its
    properties is recorded by the NameNode. An application can specify the number of replicas of a
    file that should be maintained by HDFS. The number of copies of a file is called the replication
    factor of that file. This information is stored by the NameNode.

  • Data Blocks: HDFS is designed to support very large files. Applications that are compatible with
    HDFS are those that deal with large data sets. These applications write their data only once but
    they read it one or more times and require these reads to be satisfied at streaming speeds. HDFS
    supports write-once-read-many semantics on files. A typical block size used by HDFS is 64 MB.
    Thus, an HDFS file is chopped up into 64 MB chunks, and if possible, each chunk will reside on a
    different DataNode.

  • Cluster Rebalancing: The HDFS architecture is compatible with data rebalancing schemes. A scheme
    might automatically move data from one DataNode to another if the free space on a DataNode falls
    below a certain threshold. In the event of a sudden high demand for a particular file, a scheme
    might dynamically create additional replicas and rebalance other data in the cluster. These types
    of data rebalancing schemes are not yet implemented.

References and sources
  1. Apache Hadoop on wikipedia
  2. HDFS architecture
  3. HDFS 3.3.1 commands guide

Troubleshooting section

Data is priceless. Before you perform any action, make sure that you have taken any necessary backup
steps. Netdata is not liable for any loss or corruption of any data, database, or software.

Identify which DataNode has a failing volume and troubleshoot it

For Apache Hadoop 3.3.1 3

  1. Identify which DataNodes are offline, these are the sources of your problems:

     root@netdata # dfsadmin -report

    Identify which node is not reported in the output of the above command. If all the nodes are there, then you must run the next command for each DataNode.

  2. Review the volumes status:

    root@netdata # hdfs dfsadmin -getVolumeReport datanodehost:port
  3. Connect to the node and inspect the journalctl -xe. If you have the Netdata Agent running in the DataNodes as well, you should be able to identify the problem. You should be receiving alarms about the disks and the mounts in this system.