Hadoop and HDFS Overview. Madhu Ankam
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1 Hadoop and HDFS Overview Madhu Ankam
2 Why Hadoop We are gathering more data than ever Examples of data : Server logs Web logs Financial transactions Analytics s and text messages Social media like Facebook, twitter etc. Data is value and we must process that to extract the value. 4/27/2016 Madhu Ankam 2
3 Data Accessibility How can we process all that information There are actually two problems Large-scale data storage Large-scale data analysis Although we can process data more quickly (because of faster processors), accessing is slow (this is true for both reads and writes). For example, reading a single disk for terabytes of data take hours, so we cannot process the data until we have read it and we are limited by the speed of the single disk. 4/27/2016 Madhu Ankam 3
4 Data Accessibility Traditional computation is processor-bound, bigger and powerful machines. But they are highly costly and limited scalability. So the solution is distributed computing across machines using powerful compute nodes, data storage and fast network to connect. 4/27/2016 Madhu Ankam 4
5 4/27/2016 Madhu Ankam 5
6 HDFS: The Hadoop Distributed File System Based on Google s GFS (Google File System) Provides redundant storage for massive amounts of data (Using Industry-standard hardware) At load time, data is distributed across all nodes, provides for efficient processing. 4/27/2016 Madhu Ankam 6
7 HDFS Features It is suitable for the distributed storage and processing. Hadoop provides a command interface to interact with HDFS. The built-in servers of namenode and datanode help users to easily check the status of cluster. Streaming access to file system data. HDFS provides file permissions and authentication. 4/27/2016 Madhu Ankam 7
8 HDFS Design Assumptions High component failure rates inexpensive components fail all the time Files are write-once Large streaming reads, not random access 4/27/2016 Madhu Ankam 8
9 HDFS Blocks Generally the user data is stored in the files of HDFS. The file in a file system will be divided into one or more segments and/or stored in individual data nodes. These file segments are called as blocks. In other words, the minimum amount of data that HDFS can read or write is called a Block. The default block size is 64MB, but it can be increased as per the need to change in HDFS configuration. 4/27/2016 Madhu Ankam 9
10 HDFS Blocks 4/27/2016 Madhu Ankam 10
11 HDFS Replications These blocks are replicated to nodes throughout the cluster, based on the replication factor (default is three). Replication increases reliability and performance. Reliability: data can tolerate loss of all but one replica Performance : more opportunities for data locality. 4/27/2016 Madhu Ankam 11
12 HDFS Replications 4/27/2016 Madhu Ankam 12
13 HDFS without High Availability We can deploy HDFS with or without high availability Without high availability, there are three daemons 1. Namenode (master) 2. Secondary Namenode (master) 3. Datanode (slave) 4/27/2016 Madhu Ankam 13
14 Namenode The namenode is the commodity hardware that contains the GNU/Linux operating system and the namenode software. It is a software that can be run on commodity hardware. The system having the namenode acts as the master server and it does the following tasks: Manages the file system namespace. Regulates client s access to files. It also executes file system operations such as renaming, closing, and opening files and directories. 4/27/2016 Madhu Ankam 14
15 Datanode The datanode is a commodity hardware having the GNU/Linux operating system and datanode software. For every node (Commodity hardware/system) in a cluster, there will be a datanode. These nodes manage the data storage of their system. Datanodes perform read-write operations on the file systems, as per client request. They also perform operations such as block creation, deletion, and replication according to the instructions of the namenode. 4/27/2016 Madhu Ankam 15
16 Secondary Namenode The secondary namenode is not a failover namenode. It performs memory-intensive administrative functions for the namenode. Namenode keeps information about files and blocks (the metadata) in memory. Namenode writes metadata changes to an edit log Secondary namenode periodically combines a prior snapshot of the file sytem metadata and edit log into a new snapshot. New snapshot is transmitted back to the namenode. 4/27/2016 Madhu Ankam 16
17 HDFS with High Availability We can deploy HDFS with high availability to eliminate the namenode Single Point of Failure. Two Namenodes: one active and one standby. Standby namenode takes over when active namenode fails. Standby namenode also does checkpointing (secondary namenode no longer needed). 4/27/2016 Madhu Ankam 17
18 HDFS with High Availability 4/27/2016 Madhu Ankam 18
19 File Write 4/27/2016 Madhu Ankam 19
20 File Write Process Client connects to the namenode, name node places n entry for the file in its metadata, returns name and list of datanodes to the client. Client connects to the first datanode and starts sending data. As data is received by the first datanode, it connects to the second and starts sending data, second one connects to the third. Acknowledgement packets from the pipeline are sent back to the client. Client reports to the namenode when the block is written. 4/27/2016 Madhu Ankam 20
21 File Write Process If a Datanode in the pipeline fails, the pipeline is closed. A new pipeline is opened with the two good nodes. The data continues to be written to the two good nodes in the pipeline. The namenode will realize that the block is under-replicated, and will re-replicate it to another Datanode. As blocks of data are written, the client calculates a checksum for each block. 4/27/2016 Madhu Ankam 21
22 Rack Awareness Hadoop understands the concept of rack awareness The idea of where nodes are located, relative to one another Helps the resource manager allocate processing resources on nodes closest to the data Helps the namenode determine the closest block to a client during reads. HDFS replicates data blocks on nodes on different racks, provides extra data security in case of catastrophic hardware failure. 4/27/2016 Madhu Ankam 22
23 HDFS block replication strategy First copy of the block is placed on the same node as the client, if the client is not part of the cluster, the first block is placed on a random node. Second copy of the block is placed on a node residing on a different rack. Third copy of the block is placed on different node in the same rack as the second copy. 4/27/2016 Madhu Ankam 23
24 Anatomy of a File Read 4/27/2016 Madhu Ankam 24
25 Data Client connects to the namenode. Namenode returns the name and locations of the first few blocks of the file, block locations are returned closest-first. Client connects to the first of the datanodes, and reads the block. If the datanode fails during the read, the client will seamlessly connects to the next one in the list to read the block. 4/27/2016 Madhu Ankam 25
26 Dealing with Data corruption When a client reading the block, it also verifies the checksum, if they differ, the client reads from the next datanode in the list. The datanode also verifies the checksums for blocks on a regular basis to avoid corruption of data. Default is every three weeks after the block was created. 4/27/2016 Madhu Ankam 26
27 Dealing with data The data never travels via a Namenode, for writes, or reads or during the re-replication. Datanodes send heartbeats to the namenode every three seconds After a period without any heartbeats, a datanode is assumed to be lost. Then namenode finds another datanodes to replicate the data. A datanode can rejoin a cluster after being down for a period of time. 4/27/2016 Madhu Ankam 27
28 HDFS File Permissions Files in HDFS have an owner, a group and others permissions very similar to Unix/Linux file permissions. File permissions are read (r), write (w) and execute (x) for each owner, group and others. X is ignored for files by default, for directories, x means that its children can be accessed. 4/27/2016 Madhu Ankam 28
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