hdfs_stale_nodes

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hdfs_stale_nodes

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 file systems.1

Receiving this alert (in warning state) indicates that there is at least one stale DataNode due to missed heartbeats.

A stale DataNode is one that has not been reachable for dfs.namenode.stale.datanode.interval (default is 30 seconds). Stale DataNodes are avoided, and marked as the last possible target for a read or write operation. By default, HDFS marks a node as dead if it is unreachable for 630 seconds.

See more about Hadoop

Wikipedia provides a great explanation of HDFS1 . Here are the main takeaways:

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. In
    particular, the NameNode contains the details of the number of blocks, locations of
    the DataNode that the data is stored in, where the replications are stored, and other details.
    The NameNode has direct contact with the client.

  • 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 & send fsimage &
    editlog file, upon which the compacted fsimage file is created by the secondary NameNode.

  • 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

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.

Fix corrupted or missing blocks

  1. Identify the stale node(s)

    root@netdata #  hadoop dfsadmin -report

    Inspect the output and check which DataNode is stale.

  2. Connect to the DataNode and check the log of the DataNode. You can also check for errors in the
    system services.

    root@netdata #  systemctl status hadoop

    Restart the service if needed.