Some of its key functions are:. All write-operations from clients need to be routed through the leader, whereas read operations can go directly to any server. Zookeeper provides high reliability and resilience through fail-safe synchronization, atomicity, and serialization of messages.
Kafka is a distributed publish-subscribe messaging system that is often used with Hadoop for faster data transfers. A Kafka cluster consists of a group of servers that act as an intermediary between producers and consumers. In the context of big data, an example of a producer could be a sensor gathering temperature data to relay back to the server.
Consumers are the Hadoop servers. The producers publish message on a topic and the consumers pull messages by listening to the topic. A single topic can be split further into partitions. All messages with the same key arrive to a specific partition. A consumer can listen to one or more partitions. By grouping messages under one key and getting a consumer to cater to specific partitions, many consumers can listen on the same topic at the same time.
Thus, a topic is parallelized, increasing the throughput of the system. Kafka is widely adopted for its speed, scalability, and robust replication. One of the challenges with HDFS is that it can only do batch processing.
So for simple interactive queries, data still has to be processed in batches, leading to high latency. HBase solves this challenge by allowing queries for single rows across huge tables with low latency. It achieves this by internally using hash tables. HBase is scalable, has failure support when a node goes down, and is good with unstructured as well as semi-structured data.
Hence, it is ideal for querying big data stores for analytical purposes. Though Hadoop has widely been seen as a key enabler of big data, there are still some challenges to consider. These challenges stem from the nature of its complex ecosystem and the need for advanced technical knowledge to perform Hadoop functions. However, with the right integration platform and tools, the complexity is reduced significantly and hence, makes working with it easier as well.
To query the Hadoop file system, programmers have to write MapReduce functions in Java. This is not straightforward, and involves a steep learning curve. Also, there are too many components that make up the ecosystem, and it takes time to get familiar with them. There is no 'one size fits all' solution in Hadoop. Most of the supplementary components discussed above have been built in response to a gap that needed to be addressed.
For example, Hive and Pig provide a simpler way to query the data sets. Additionally, data ingestion tools such as Flume and Sqoop help gather data from multiple sources. In any given Hadoop cluster, there can only be one active name node at a time. When an active NameNode goes down, the secondary NameNode takes up responsibility. Any request to create, edit, or delete HDFS files first gets recorded in journal nodes; journal nodes are responsible for coordinating with data nodes for propagating changes.
Once the writing is complete, changes are flushed and a response is sent back to calling APIs. In case the flushing of changes in the journal files fails, the NameNode moves on to another node to record changes. DataNode in the Hadoop ecosystem is primarily responsible for storing application data in distributed and replicated form.
It acts as a slave in the system and is controlled by NameNode. Each disk in the Hadoop system is divided into multiple blocks, just like a traditional computer storage device. A block is a minimal unit in which the data can be read or written by the Hadoop filesystem.
This ecosystem gives a natural advantage in slicing large files into these blocks and storing them across multiple nodes. The default block size of data node varies from 64 MB to MB, depending upon Hadoop implementation. This can be changed through the configuration of data node. HDFS is designed to support very large file sizes and for write-once-read-many-based semantics.
Data nodes are primarily responsible for storing and retrieving these blocks when they are requested by consumers through Name Node. In Hadoop version 3. X, DataNode not only stores the data in blocks, but also the checksum or parity of the original blocks in a distributed manner. DataNodes follow the replication pipeline mechanism to store data in chunks propagating portions to other data nodes.
When a cluster starts, NameNode starts in a safe mode, until the data nodes register the data block information with NameNode.
Once this is validated, it starts engaging with clients for serving the requests. When a data node starts, it first connects with Name Node, reporting all of the information about its data blocks' availability. This information is registered in NameNode, and when a client requests information about a certain block, NameNode points to the respective data not from its registry.
During the cluster processing, data node communicates with name node periodically, sending a heartbeat signal. The frequency of the heartbeat can be configured through configuration files. We have gone through different key architecture components of the Apache Hadoop framework; we will be getting a deeper understanding in each of these areas in the next chapters.
Apache Hadoop development is happening on multiple tracks. The releases of 2. X, and 3. X were simultaneous. Hadoop 3. X was separated from Hadoop 2. We will look at major improvements in the latest releases: 3. X and 2. Erasure Code EC is a one of the major features of the Hadoop 3. X release. It changes the way HDFS stores data blocks. In earlier implementations, the replication of data blocks was achieved by creating replicas of blocks on different node.
In the case of EC, instead of replicating the data blocks, it creates parity blocks. In this case, for three blocks of data, the system would create two parity blocks, resulting in a total of MB, which is approximately Although EC achieves significant gain on data storage, it requires additional computing to recover data blocks in case of corruption, slowing down recovery with respect to the traditional way in old Hadoop versions. We have already seen multiple secondary Name Node support in the architecture section.
Intra-Data Node Balancer is used to balance skewed data resulting from the addition or replacement of disks among Hadoop slave nodes.
This balancer can be explicitly called from the HDFS shell asynchronously. This can be used when new nodes are added to the system. In Hadoop v3, YARN Scheduler has been improved in terms of its scheduling strategies and prioritization between queues and applications. Scheduling can be performed among the most eligible nodes rather than one node at a time, driven by heartbeat reporting, as in older versions.
YARN is being enhanced with abstract framework to support long-running services ; it provides features to manage the life cycle of these services and support upgrades, resizing containers dynamically rather than statically. Another major enhancement is the release of Application Timeline Service v2.
This service now supports multiple instances of readers and writes compared to single instances in older Hadoop versions with pluggable storage options. The overall metric computation can be done in real time, and it can perform aggregations on collected information.
YARN User Interface is enhanced significantly, for example, to show better statistics and more information, such as queue. Hadoop version 3 and above allows developers to define new resource types earlier there were only two managed resources: CPU and memory. This enables applications to consider GPUs and disks as resources too. There have been new proposals to allow static resources such as hardware profiles and software versions to be part of the resourcing. Docker has been one of the most successful container applications that the world has adapted rapidly.
So, YARN can be deployed in dockerized containers, giving a complete isolation of tasks. This enhancement is intended to improve the performance of MapReduce tasks by two to three times. This feature allows a very large cluster to be divided into multiple sub-clusters, each running YARN Resource Manager and computations. Another interesting enhancement is migration to newer JDK 8. Here is the supportability matrix for previous and new Hadoop versions and JDK:.
Earlier, applications often had conflicts due to the single JAR file; however, the new release has two separate jar libraries: server side and client side. Since being created and open sourced by LinkedIn in , Kafka has quickly evolved from messaging queue to a full-fledged event streaming platform. Founded by the original developers of Apache Kafka, Confluent delivers the most complete distribution of Kafka with Confluent Platform.
Confluent Platform improves Kafka with additional community and commercial features designed to enhance the streaming experience of both operators and developers in production, at massive scale.
At its heart lies the humble, immutable commit log, and from there you can subscribe to it, and publish data to any number of systems or real-time applications. Unlike messaging queues, Kafka is a highly scalable, fault tolerant distributed system, allowing it to be deployed for applications like managing passenger and driver matching at Uber, providing real-time analytics and predictive maintenance for British Gas' smart home, and performing numerous real-time services across all of LinkedIn.
This unique performance makes it perfect to scale from one app to company-wide use. An abstraction of a distributed commit log commonly found in distributed databases, Apache Kafka provides durable storage. Kafka can act as a 'source of truth', being able to distribute data across multiple nodes for a highly available deployment within a single data center or across multiple availability zones.
The group founders set the original rules, but they can be changed by vote of the active PMC members. There is a group of people who have logins on our server and access to the source code repositories.
Everyone has read-only access to the repositories. Our primary method of communication is our mailing list. Approximately 40 messages a day flow over the list, and are typically very conversational in tone. We discuss new features to add, bug fixes, user problems, developments in the web server community, release dates, etc.
The actual code development takes place on the developers' local machines, with proposed changes communicated using a patch output of a unified "diff -u oldfile newfile" command , and then applied to the source code control repositories by one of the committers. Anyone on the mailing list can vote on a particular issue, but only those made by active members or people who are known to be experts on that part of the server are counted towards the requirements for committing.
Vetoes must be accompanied by a convincing technical justification. New members of the Apache HTTP Project Management Committee are added when a frequent contributor is nominated by one member and unanimously approved by the voting members. In most cases, this "new" member has been actively contributing to the group's work for over six months, so it's usually an easy decision.
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