Hadoop Training

What is Hadoop?   

          Hadoop is a platform that provides both distributed storage and computational capabilities.

Hadoop was first conceived to fix a scalability issue that existed in Nutch, an open source crawler and search engine.

At the time Google had published papers that described its novel distributed filesystem, the Google File System (GFS), and Map- Reduce, a computational framework for parallel processing.

The successful implementation of these papers concepts in Nutch resulted in its split into two separate projects, the second of which became Hadoop, a first-class Apache project.

Hadoop is a distributed master-slave architecture that consists of the Hadoop Distributed File System (HDFS) for storage and Map- Reduce for computational capabilities.

          Hadoop has two major components:

- the distributed filesystem component, the main example of which is the Hadoop Distributed File System, though other file systems are supported.

- the MapReduce component, which is a framework for performing calculations on the data in the distributed file system.

          A node is simply a computer, typically non-enterprise, commodity hardware for nodes that contain data. So in this example, we have Node 1. Then we can add more nodes, such as Node 2, Node 3, and so on. This would be called a rack.

A rack is a collection of 30 or 40 nodes that are physically stored close together and are all connected to the same network switch.

Network bandwidth between any two nodes in rack is greater than bandwidth between two nodes on different racks.

A Hadoop Cluster (or just ‘cluster’ from now on) is a collection of racks

  

Apache Hadoop Fundamentals – HDFS and 

MapReduce

Hadoop is an open source software used for distributed computing that can be used to query a large set of data and get the results faster using reliable and scalable architecture.

          In a traditional non distributed architecture, you’ll have data stored in one server and any client program will access this central data server to retrieve the data.

The non distributed model has few fundamental issues. In this model, you’ll mostly scale vertically by adding more CPU, adding more storage, etc.

This architecture is also not reliable, as if the main server fails, you have to go back to the backup to restore the data.

From performance point of view, this architecture will not provide the results faster when you are running a query against a huge data set.

         

          In a hadoop distributed architecture, both data and processing are distributed across multiple servers. The following are some of the key points to remember about the hadoop:

•   Each and every server offers local computation and storage. i.e When you run a query against a large data set, every server in this distributed architecture will be executing the query on its local machine against the local data set. Finally, the resultset from all this local servers are consolidated.
•  In simple terms, instead of running a query on a single server, the query is split across multiple servers, and the results are consolidated. This means that the results of a query on a larger dataset are returned faster.
•   You don’t need a powerful server. Just use several less expensive commodity servers as hadoop individual nodes.
•   High fault-tolerance. If any of the nodes fails in the hadoop environment, it will still return the dataset properly, as hadoop takes care of replicating and distributing the data efficiently across the multiple nodes.
• A simple hadoop implementation can use just two servers. But you can scale up to several thousands of servers without any additional effort.
•   Hadoop is written in Java. So, it can run on any platform.
•  Please keep in mind that hadoop is not a replacement for your RDBMS. You’ll typically use hadoop for unstructured data
•   Originally Google started using the distributed computing model based on GFS (Google Filesystem) and MapReduce. Later Nutch (open source web search software) was rewritten using MapReduce. Hadoop was branced out of Nutch as a separate project. Now Hadoop is a top-level Apache project that has gained tremendous momentum and popularity in recent years.

HDFS

HDFS stands for Hadoop Distributed File System, which is the storage system used by Hadoop. The following is a high-level architecture that explains how HDFS works.

The following are some of the key points to remember about the HDFS:

•   In the above diagram, there is one NameNode, and multiple DataNodes (servers). b1, b2, indicates data blocks.
•   When you dump a file (or data) into the HDFS, it stores them in blocks on the various nodes in the hadoop cluster. HDFS creates several replication of the data blocks and distributes them accordingly in the cluster in way that will be reliable and can be retrieved faster. A typical HDFS block size is 128MB. Each and every data block is replicated to multiple nodes across the cluster.
•   Hadoop will internally make sure that any node failure will never results in a data loss.
•   There will be one NameNode that manages the file system metadata
•   There will be multiple DataNodes (These are the real cheap commodity servers) that will store the data blocks
•   When you execute a query from a client, it will reach out to the NameNode to get the file metadata information, and then it will reach out to the DataNodes to get the real data blocks
•  Hadoop provides a command line interface for administrators to work on HDFS
•   The NameNode comes with an in-built web server from where you can browse the HDFS filesystem and view some basic cluster statistics
•  MapReduce

The following are some of the key points to remember about the HDFS:

•   MapReduce is a parallel programming model that is used to retrieve the data from the Hadoop cluster
•  In this model, the library handles lot of messy details that programmers doesn’t need to worry about. For example, the library takes care of parallelization, fault tolerance, data distribution, load balancing, etc.
•   This splits the tasks and executes on the various nodes parallely, thus speeding up the computation and retriving required data from a huge dataset in a fast manner.
•   This provides a clear abstraction for programmers. They have to just implement (or use) two functions: map and reduce
• The data are fed into the map function as key value pairs to produce intermediate key/value pairs
•   Once the mapping is done, all the intermediate results from various nodes are reduced to create the final output
• JobTracker keeps track of all the MapReduces jobs that are running on various nodes. This schedules the jobs, keeps track of all the map and reduce jobs running across the nodes. If any one of those jobs fails, it reallocates the job to another node, etc. In simple terms, JobTracker is responsible for making sure that the query on a huge dataset runs successfully and the data is returned to the client in a reliable manner.
•  TaskTracker performs the map and reduce tasks that are assigned by the JobTracker. TaskTracker also constantly sends a hearbeat message to JobTracker, which helps JobTracker to decide whether to delegate a new task to this particular node or not.  

Data Organization

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.

Staging

A client request to create a file does not reach the NameNode immediately. In fact, initially the HDFS client caches the file data into a temporary local file. Application writes are transparently redirected to this temporary local file. When the local file accumulates data worth over one HDFS block size, the client contacts the NameNode. The NameNode inserts the file name into the file system hierarchy and allocates a data block for it. The NameNode responds to the client request with the identity of the DataNode and the destination data block. Then the client flushes the block of data from the local temporary file to the specified DataNode. When a file is closed, the remaining un-flushed data in the temporary local file is transferred to the DataNode. The client then tells the NameNode that the file is closed. At this point, the NameNode commits the file creation operation into a persistent store. If the NameNode dies before the file is closed, the file is lost.

The above approach has been adopted after careful consideration of target applications that run on HDFS. These applications need streaming writes to files. If a client writes to a remote file directly without any client side buffering, the network speed and the congestion in the network impacts throughput considerably. This approach is not without precedent. Earlier distributed file systems, e.g. AFS, have used client side caching to improve performance. A POSIX requirement has been relaxed to achieve higher performance of data uploads.

Replication Pipelining

When a client is writing data to an HDFS file, its data is first written to a local file as explained in the previous section. Suppose the HDFS file has a replication factor of three. When the local file accumulates a full block of user data, the client retrieves a list of DataNodes from the NameNode. This list contains the DataNodes that will host a replica of that block. The client then flushes the data block to the first DataNode. The first DataNode starts receiving the data in small portions (4 KB), writes each portion to its local repository and transfers that portion to the second DataNode in the list. The second DataNode, in turn starts receiving each portion of the data block, writes that portion to its repository and then flushes that portion to the third DataNode. Finally, the third DataNode writes the data to its local repository. Thus, a DataNode can be receiving data from the previous one in the pipeline and at the same time forwarding data to the next one in the pipeline. Thus, the data is pipelined from one DataNode to the next.

HDFS has a master/slave architecture. An HDFS cluster consists of a single NameNode, a master server that manages the file system namespace and regulates access to files by clients. In addition, there are a number of DataNodes, usually one per node in the cluster, which manage storage attached to the nodes that they run on. HDFS exposes a file system namespace and allows user data to be stored in files. Internally, a file is split into one or more blocks and these blocks are stored in a set of DataNodes. The NameNode executes file system namespace operations like opening, closing, and renaming files and directories. It also determines the mapping of blocks to DataNodes. The DataNodes are responsible for serving read and write requests from the file system’s clients. The DataNodes also perform block creation, deletion, and replication upon instruction from the NameNode.

The NameNode and DataNode are pieces of software designed to run on commodity machines. These machines typically run a GNU/Linux operating system (OS). HDFS is built using the Java language; any machine that supports Java can run the NameNode or the DataNode software. Usage of the highly portable Java language means that HDFS can be deployed on a wide range of machines. A typical deployment has a dedicated machine that runs only the NameNode software. Each of the other machines in the cluster runs one instance of the DataNode software. The architecture does not preclude running multiple DataNodes on the same machine but in a real deployment that is rarely the case.

The existence of a single NameNode in a cluster greatly simplifies the architecture of the system. The NameNode is the arbitrator and repository for all HDFS metadata. The system is designed in such a way that user data never flows through the NameNode.

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 or access permissions. 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 Replication

HDFS is designed to reliably store very large files across machines in a large cluster. It stores each file as a sequence of blocks; all blocks in a file except the last block are the same size. The blocks of a file are replicated for fault tolerance. The block size and replication factor are configurable per file. An application can specify the number of replicas of a file. The replication factor can be specified at file creation time and can be changed later. Files in HDFS are write-once and have strictly one writer at any time.

The NameNode makes all decisions regarding replication of blocks. It periodically receives a Heartbeat and a Blockreport from each of the DataNodes in the cluster. Receipt of a Heartbeat implies that the DataNode is functioning properly. A Blockreport contains a list of all blocks on a DataNode.

Replica Placement:

The placement of replicas is critical to HDFS reliability and performance. Optimizing replica placement distinguishes HDFS from most other distributed file systems. This is a feature that needs lots of tuning and experience. The purpose of a rack-aware replica placement policy is to improve data reliability, availability, and network bandwidth utilization. The current implementation for the replica placement policy is a first effort in this direction. The short-term goals of implementing this policy are to validate it on production systems, learn more about its behavior, and build a foundation to test and research more sophisticated policies.

Large HDFS instances run on a cluster of computers that commonly spread across many racks. Communication between two nodes in different racks has to go through switches. In most cases, network bandwidth between machines in the same rack is greater than network bandwidth between machines in different racks.

The NameNode determines the rack id each DataNode belongs to via the process outlined in Rack Awareness. A simple but non-optimal policy is to place replicas on unique racks. This prevents losing data when an entire rack fails and allows use of bandwidth from multiple racks when reading data. This policy evenly distributes replicas in the cluster which makes it easy to balance load on component failure. However, this policy increases the cost of writes because a write needs to transfer blocks to multiple racks.

For the common case, when the replication factor is three, HDFS’s placement policy is to put one replica on one node in the local rack, another on a different node in the local rack, and the last on a different node in a different rack. This policy cuts the inter-rack write traffic which generally improves write performance. The chance of rack failure is far less than that of node failure; this policy does not impact data reliability and availability guarantees. However, it does reduce the aggregate network bandwidth used when reading data since a block is placed in only two unique racks rather than three. With this policy, the replicas of a file do not evenly distribute across the racks. One third of replicas are on one node, two thirds of replicas are on one rack, and the other third are evenly distributed across the remaining racks. This policy improves write performance without compromising data reliability or read performance.

The current, default replica placement policy described here is a work in progress.

Replica Selection

To minimize global bandwidth consumption and read latency, HDFS tries to satisfy a read request from a replica that is closest to the reader. If there exists a replica on the same rack as the reader node, then that replica is preferred to satisfy the read request. If angg/ HDFS cluster spans multiple data centers, then a replica that is resident in the local data center is preferred over any remote replica.

Safemode

On startup, the NameNode enters a special state called Safemode. Replication of data blocks does not occur when the NameNode is in the Safemode state. The NameNode receives Heartbeat and Blockreport messages from the DataNodes. A Blockreport contains the list of data blocks that a DataNode is hosting. Each block has a specified minimum number of replicas. A block is considered safely replicated when the minimum number of replicas of that data block has checked in with the NameNode. After a configurable percentage of safely replicated data blocks checks in with the NameNode (plus an additional 30 seconds), the NameNode exits the Safemode state. It then determines the list of data blocks (if any) that still have fewer than the specified number of replicas. The NameNode then replicates these blocks to other DataNodes.

The Persistence of File System Metadata

The HDFS namespace is stored by the NameNode. The NameNode uses a transaction log called the EditLog to persistently record every change that occurs to file system metadata. For example, creating a new file in HDFS causes the NameNode to insert a record into the EditLog indicating this. Similarly, changing the replication factor of a file causes a new record to be inserted into the EditLog. The NameNode uses a file in its local host OS file system to store the EditLog. The entire file system namespace, including the mapping of blocks to files and file system properties, is stored in a file called the FsImage. The FsImage is stored as a file in the NameNode’s local file system too.

The NameNode keeps an image of the entire file system namespace and file Blockmap in memory. This key metadata item is designed to be compact, such that a NameNode with 4 GB of RAM is plenty to support a huge number of files and directories. When the NameNode starts up, it reads the FsImage and EditLog from disk, applies all the transactions from the EditLog to the in-memory representation of the FsImage, and flushes out this new version into a new FsImage on disk. It can then truncate the old EditLog because its transactions have been applied to the persistent FsImage. This process is called a checkpoint. In the current implementation, a checkpoint only occurs when the NameNode starts up. Work is in progress to support periodic checkpointing in the near future.

The DataNode stores HDFS data in files in its local file system. The DataNode has no knowledge about HDFS files. It stores each block of HDFS data in a separate file in its local file system. The DataNode does not create all files in the same directory. Instead, it uses a heuristic to determine the optimal number of files per directory and creates subdirectories appropriately. It is not optimal to create all local files in the same directory because the local file system might not be able to efficiently support a huge number of files in a single directory. When a DataNode starts up, it scans through its local file system, generates a list of all HDFS data blocks that correspond to each of these local files and sends this report to the NameNode: this is the Blockreport.

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