Hadoop Distributed File System (HDFS) Overview

A distributed file system designed for commodity hardware, providing a POSIX-like interface for large-scale data storage and retrieval.

HDFS prioritizes handling faults and recovering quickly from them, recognizing that failures are common in large-scale systems.

HDFS is optimized for batch processing tasks like MapReduce, favoring high throughput over low latency for data access.

HDFS deals with files in the range of gigabytes to terabytes, prioritizing high aggregate bandwidth, scalability, and support for massive file counts.

HDFS utilizes a write-once-read-many (WORM) model, simplifying data coherency and allowing for high throughput data access.

HDFS supports appends and truncate operations for files but restricts modification, simplifying data coherency and enhancing performance.

HDFS is optimized for batch processing and large datasets, prioritizing high-volume throughput over low latency. It focuses on efficiently handling data for large-scale analytics.

HDFS relies on commodity hardware, which means it's designed to work with inexpensive, readily available components.

HDFS constantly checks for bad blocks, fixing them before client access. It scans blocks for checksum errors, one by one, and uses throttling to avoid overwhelming the DataNode.

The Hadoop FS shell provides commands for interacting directly with HDFS. It lets you create directories, list files, upload data, and read files from HDFS.

A mechanism that prevents a task from consuming all available resources (e.g., bandwidth) at once, ensuring other users or processes can access them.

A block in HDFS that has become corrupted, possibly due to disk errors. It can cause issues when trying to access the data.

A process that involves calculating a checksum for each block. The checksum is then compared to the original checksum to ensure the block is not corrupted.

A mechanism in HDFS to record all changes made to the filesystem since the last checkpoint.

A snapshot of the HDFS namespace at a particular point in time. It's used to restore the filesystem if something goes wrong.

A node in HDFS that periodically merges the journal and checkpoint to create a new checkpoint and sends it to the NameNode.

A node in HDFS that maintains a read-only copy of the NameNode's namespace data, without block locations.

A mechanism in HDFS to ensure data availability even if a DataNode fails or a block is corrupted.

A node in HDFS that stores data blocks and handles data requests.

A process in HDFS to improve read performance by storing frequently accessed blocks in a DataNode's memory.

A feature in HDFS that allows scaling by adding more NameNodes to manage larger namespaces.

A feature in HDFS that allows for high availability by having two NameNodes, one active and one standby, to ensure continuous operation.

A mechanism in HDFS that ensures the previous Active NameNode is stopped and prevents it from causing corruption during a failover.

A process in HDFS that balances the distribution of blocks across DataNodes to improve performance and data locality.

A daemon running on each DataNode that periodically checks the blocks stored on it to ensure data integrity.

A system in HDFS that ensures data availability by storing multiple replicas of each block on different DataNodes.

The process of transferring data between HDFS nodes, like moving blocks from one DataNode to another.

A design principle for distributed systems, where it's typically more efficient to move the computation to the data rather than moving the data to the computation.

A component that's responsible for managing file system metadata (e.g., file names, directories, sizes, timestamps, etc.).

A component that stores actual data blocks in HDFS.

Large files in HDFS are broken down into these chunks for easier management and efficient distribution.

The process of creating multiple copies of a block across different DataNodes for fault tolerance and availability.

The number of replicas of a file that HDFS maintains, typically set to 3 for data redundancy.

A library used by applications to interact with the HDFS file system interface.

DataNodes are selected to host replicas of a file based on their network topology, aiming to minimize write cost and optimize read efficiency.

A mechanism where the NameNode grants a lease (lock) to a single client to write to a file, while allowing multiple clients to read it concurrently.

A mechanism used by the NameNode to persist file system metadata as checkpoints and journal entries.

A model that describes how reads and writes are made visible to clients in a file system. HDFS sacrifices some POSIX semantics for performance.

A function that ensures all data written to a file has reached all DataNodes in the write pipeline and is visible to new readers.

A function that guarantees that all data written to a file has been written to disk by DataNodes.

A hierarchical structure of files and directories, which supports user quotas and access permissions.

The process of moving computation logic closer to the data to optimize performance and minimize network traffic.