# hdfs\_num\_failed\_volumes **URL:** \ **Category:** Alerts\ **Created:** [November 3, 2021, 9:57pm UTC](https://community.netdata.cloud/t/hdfs-num-failed-volumes/1990 "2021-11-03T21:57:35Z")\ **Posts on this page:** 1\ **Page:** 1
**Author:** ![Tasos\_Katsoulas](https://yyz1.discourse-cdn.com/flex029/user_avatar/community.netdata.cloud/tasos_katsoulas/32/1344_2.png) [@Tasos\_Katsoulas](https://community.netdata.cloud/u/Tasos_Katsoulas)\ **Post date:** [November 3, 2021, 9:57pm UTC](https://community.netdata.cloud/t/hdfs-num-failed-volumes/1990/1 "2021-11-03T21:57:35Z")
# hdfs\_num\_failed\_volumes **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](https://en.wikipedia.org/wiki/Apache_Hadoop) 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](https://en.wikipedia.org/wiki/Apache_Hadoop) , here you can see some highlight points > > HDFS provides a software framework for distributed storage and processing of big data using the`MapReduce` 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: > > - 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](https://hadoop.apache.org/docs/r1.2.1/hdfs_design.html) : > > - 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. > > - 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](https://en.wikipedia.org/wiki/Apache_Hadoop) > 2. [HDFS architecture](https://hadoop.apache.org/docs/r1.2.1/hdfs_design.html) > 3. [HDFS 3.3.1 commands guide](https://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-hdfs/HDFSCommands.html) ### 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](https://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-hdfs/HDFSCommands.html) > > 1. Identify which DataNodes are offline, these are the sources of your problems: > > 2. Review the volumes status: > > 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.