bigdatausinghadoopandhive_summary

The book “Big Data Using Hadoop and Hive” serves as an essential guide for developers, architects, and engineers interested in leveraging Hadoop for building distributed, scalable applications. It covers Hadoop 3 and Hive 3.x, detailing their architecture, functionalities, and practical applications.

Big Data Challenges and Opportunities Big Data refers to the vast amounts of structured and unstructured data generated through social interactions, online activities, and connected devices. Organizations face challenges in managing and utilizing this data due to its sheer volume and complexity. Traditional RDBMS systems are inadequate for handling such data, leading to the need for technologies like Hadoop. Big Data offers opportunities for innovation, allowing businesses to transform data into actionable insights for decision-making, predictive analysis, and recommendation systems.

Hadoop as a Solution Hadoop is an open-source framework designed to store and process large data sets across distributed environments. It provides a scalable, reliable platform for data processing, making it suitable for handling Big Data challenges. Hadoop’s ecosystem includes components like HDFS (Hadoop Distributed File System) and YARN (Yet Another Resource Negotiator), which facilitate data storage, processing, and resource management.

Key Features of Hadoop and Hive

• HDFS: A core component of Hadoop, HDFS is designed for fault tolerance and high throughput access to large data sets. It supports data replication and locality, ensuring data is stored efficiently across clusters.
• YARN: Manages resources and scheduling for Hadoop applications, improving upon limitations of earlier Hadoop versions.
• MapReduce: A programming model for processing large data sets with a distributed algorithm on a cluster.
• Hive: A data warehouse infrastructure built on top of Hadoop, Hive facilitates querying and managing large data sets using a SQL-like language. It supports complex data types and provides features like partitioning and bucketing for efficient data handling.

Applications of Big Data and Hadoop Hadoop is used in various real-world applications such as:

• Data Transformation: Processing large data sets from different sources.
• Market Analysis: Analyzing trends to optimize strategies.
• Machine Learning: Building systems that learn from data for AI applications.
• Recommendation Systems: Utilizing user data to suggest products or services.
• Predictive Analytics: Using data to forecast future trends and behaviors.

File Formats and Compression The book discusses various file formats like ORC and Parquet, which are optimized for storage and retrieval in Hadoop environments. It also covers data compression techniques that enhance storage efficiency and processing speed.

Overall, the book provides a comprehensive overview of Hadoop and Hive, offering insights into their capabilities and applications in managing Big Data. It is a valuable resource for professionals aiming to harness the power of Big Data technologies.

Hadoop is a robust framework for distributed storage and processing of large datasets, offering fault tolerance, scalability, and ease of use. Initially developed from Nutch by Doug Cutting, Hadoop evolved with contributions from Yahoo! and was released as an Apache project in 2008. It processes data by dividing files into smaller parts distributed across multiple nodes, enhancing processing speed and reliability.

Key Benefits of Hadoop

• Massive Data Processing: Efficiently processes terabytes to petabytes of data.
• Cost-Effective: Uses low-cost hardware for high-performance computing.
• Scalability: Easily scales storage and processing power without affecting applications.
• Reliability: Detects failures and replicates data to ensure high availability.
• Fault Tolerance: Handles node failures by reallocating tasks and replicating data.
• Fast Processing: Parallel processing reduces overall data processing time.

Hadoop Ecosystem Components

• HDFS (Hadoop Distributed File System): Core storage system that partitions data across cluster nodes. It uses a block-structured filesystem to manage large datasets.
• MapReduce: Central to Hadoop’s processing capability, handling large datasets in parallel by breaking them into independent parts for processing.
• YARN: Manages resources and job scheduling, introduced in Hadoop 2.
• Apache Spark: Provides fast in-memory data processing and supports various processing needs.
• Hive: SQL-like language for querying data stored in Hadoop, translating queries into MapReduce jobs.
• Pig: Scripting language for dataset analysis, simplifying MapReduce program sequences.
• HBase: Real-time data processing database built on HDFS.
• Oozie: Workflow system for managing multiple MapReduce job flows.
• Zookeeper: Coordination service for managing Hadoop operations.

HDFS Architecture

HDFS is designed for large-scale data storage and management in a distributed environment. It breaks files into blocks stored across DataNodes, with metadata managed by a central NameNode. This architecture supports high throughput and fault tolerance through data replication.

• NameNode: Manages metadata and file system namespace, crucial for data access and integrity.
• DataNodes: Store and replicate data blocks, handling read/write requests.
• Replication: Ensures data availability and fault tolerance by storing multiple copies of data blocks across different nodes.

Data Handling and Processing

Hadoop’s architecture supports batch and real-time processing. Data is ingested via various mechanisms like Flume and Sqoop, stored in HDFS, and processed using frameworks like MapReduce and Spark. The system’s design minimizes network congestion by processing data locally.

Overall, Hadoop provides a comprehensive solution for big data challenges, integrating various components to handle data storage, processing, and management efficiently. Its ability to process large datasets reliably and cost-effectively makes it a pivotal tool in big data analytics.

Hadoop’s NameNode uses rack awareness to enhance data availability and fault tolerance by distributing data replicas across different racks. This strategy mitigates rack failures but increases I/O costs due to inter-rack data transfer. Typically, with a replication factor of three, one block is stored locally, and two replicas are placed on a different rack, improving performance by reducing inter-traffic costs. However, this doesn’t guarantee even data distribution across racks.

Hadoop’s execution paradigm relies on data locality, which prioritizes processing tasks on the same node where data resides, minimizing unnecessary data transfers and enhancing pipeline execution performance. If a task cannot execute on the same DataNode, it seeks available slots on the same rack or off-rack, maintaining efficient data processing.

Hadoop’s storage management involves various storage types like DISK, SSD, and RAM, each suited for different data access frequencies. Storage policies in HDFS allow data to be stored in hot, warm, or cold storage, optimizing data usage and access. Policies dictate storage types based on space availability, using fallback types when necessary.

Failure handling in HDFS involves mechanisms for DataNode and NameNode failures. DataNode failures trigger block re-replication, while NameNode failures, a single point of failure (SPOF), require solutions like secondary NameNode failover or active/passive failover for high availability. The Quorum Journal Manager (QJM) facilitates high availability by synchronizing states between active and standby NameNodes, ensuring continuous data access and management.

Erasure Coding (EC) in Hadoop 3.0 addresses storage overhead by reducing the replication factor, thereby decreasing storage requirements while maintaining data durability. EC uses parity bits to reconstruct lost data, significantly improving storage efficiency compared to traditional replication methods.

HDFS Disk Balancer addresses data volume imbalances across disks, improving overall performance. It redistributes data based on available space or round-robin policies, ensuring efficient disk I/O operations. The Disk Balancer tool requires planning and execution to evenly distribute data across all disks in a cluster.

The Hadoop Distributed File System (HDFS) architecture traditionally uses a single NameNode to manage files, directories, and blocks, which can become a bottleneck in large clusters. The NameNode is responsible for maintaining the file system namespace and metadata, while DataNodes store the actual data blocks. HDFS employs a master-slave topology, where the NameNode acts as the master and DataNodes as slaves. The architecture consists of two layers: the NameSpace layer, which handles file operations, and the block storage layer, which manages block operations and physical storage.

However, this single NameNode setup poses scalability and isolation challenges. To address these, HDFS Federation was introduced, allowing multiple independent NameNodes, each managing its own namespace. This federation enables horizontal scaling, improved performance, availability, and isolation by separating the namespace and storage layers. Each DataNode registers with all NameNodes, and federated NameNodes do not coordinate with each other, enhancing modularity and scalability.

HDFS processes involve reading and writing data through a sequence of steps. For reading, the client interacts with the NameNode to obtain DataNode locations, using FSDataInputStream to stream data. Writing involves creating a file via DistributedFileSystem, with data streamed through a pipeline of DataNodes. Failure handling mechanisms ensure data integrity during read/write operations by rerouting to alternative nodes if a failure occurs.

Hadoop can be set up in three modes: Local, Pseudo-Distributed, and Distributed, with the latter being suitable for production environments. Setting up Hadoop involves configuring core-site.xml, hdfs-site.xml, and mapred-site.xml for MapReduce and YARN. Key configurations include buffer size (io.file.buffer.size), block size (dfs.block.size), and reserved space (dfs.datanode.du.reserved). Security features can be implemented by specifying allowed and excluded hosts (dfs.hosts, dfs.hosts.exclude).

The command-line interface provides access to HDFS for file operations, using commands similar to Unix. Configuration parameters can be adjusted to optimize performance and security, with features like trash management (fs.trash.interval) to recover deleted files. The HDFS Federation architecture is crucial for scaling NameNodes and maintaining backward compatibility with single NameNode setups.

Overall, HDFS Federation enhances the scalability, performance, and modularity of Hadoop, addressing the limitations of the traditional single NameNode architecture by enabling multiple, independent NameNodes to manage separate namespaces in a cluster environment.

Hadoop File System (HDFS) Commands and Utilities

The Hadoop Distributed File System (HDFS) provides a command-line interface (CLI) to perform various file operations. These commands interact with HDFS and other supported file systems like Local FS, S3 FS, etc. Below are key commands and utilities:

HDFS CLI Commands

• File Operations:

  • appendToFile: Appends local files to HDFS.
  • cat: Displays file contents.
  • checksum: Retrieves file checksum.
  • chgrp, chmod, chown: Modify file permissions, group, and owner.
  • copyFromLocal, copyToLocal: Copy files between local and HDFS.
  • du, dus: Display file/directory sizes.
  • ls, lsr: List directory contents.
  • mkdir: Create directories.
  • mv, rm, rmr: Move and delete files.

• Advanced Operations:

  • setrep: Adjust replication factor.
  • stat: Get file status.
  • tail: Show the last part of a file.
  • test: Check file existence, size, or type.
  • text: Convert files to text format.
  • touchz: Create zero-length files.

Distributed Copy (DistCp)

DistCp is a utility for copying large datasets across or within clusters using MapReduce. It supports update and overwrite options, ensuring efficient data transfer by only copying changed or non-existing files.

HDFS Permissions and Quotas

HDFS implements a POSIX-like permission model with read, write, and execute permissions. It also supports Access Control Lists (ACLs) for fine-grained access control. Quotas can be set for space and file counts to manage resource usage effectively.

Short-Circuit Local Reads

Short-circuit reads allow clients to bypass the DataNode for local data access, enhancing performance. Configuration involves setting parameters in hdfs-default.xml.

Offline Tools

• Edits Viewer: Converts edit logs to XML or binary for analysis.
• Image Viewer: Transforms fsimage files into human-readable formats.

WebHDFS

WebHDFS provides a RESTful API for HDFS operations, supporting HTTP methods like GET, POST, PUT, and DELETE. It is secure with Kerberos and token-based authentication.

Java and C APIs

Hadoop offers APIs for Java and C to interact with HDFS. The Java API mimics the standard file system interface, while libhdfs allows non-Java programs to access HDFS.

Configuration

Key configurations include Kerberos authentication settings and socket timeouts. WebHDFS URIs follow a pattern similar to HTTP URIs, supporting various operations such as file creation, deletion, and permission management.

Summary

Hadoop’s HDFS provides versatile command-line tools and APIs for managing large datasets. It supports complex operations like distributed copying and permission management, ensuring efficient and secure data handling in a distributed environment. The use of RESTful APIs and offline tools further enhances its accessibility and usability for developers and administrators.

The text provides a detailed overview of Hadoop’s HDFS and YARN, focusing on file operations, authentication, Java APIs, and resource management.

HDFS File Operations

• Delegation Tokens: Operations include renewing (RENEWDELEGATIONTOKEN) and canceling (CANCELDELEGATIONTOKEN) tokens.
• Snapshots: Create (CREATESNAPSHOT), rename (RENAMESNAPSHOT), and delete (DELETESNAPSHOT) snapshots.
• Extended Attributes (XAttr): Set (SETXATTR) and remove (REMOVEXATTR) XAttrs.
• Storage Policies: Set (SETSTORAGEPOLICY) and unset (UNSETSTORAGEPOLICY) storage policies.
• File Operations: Append (APPEND), concatenate (CONCAT), truncate (TRUNCATE), and delete (DELETE) files and directories.

Error Responses

• HTTP Error Codes: Maps exceptions like IllegalArgumentException (400), SecurityException (401), IOException (403), and FileNotFoundException (404) to HTTP response codes.

Authentication

• Methods: Uses HTTP tokens and Kerberos SPNEGO. If security is disabled, usernames can be specified in requests.
• Curl Examples: Demonstrates various authentication methods using curl, including when security is on or off.

Java FileSystem API

• FileSystem Class: Accesses and manages HDFS files using org.apache.hadoop.fs.FileSystem.
• Block Management: Files are stored in blocks, managed by NameNode, and accessed via DataNodes.
• FileSystem Implementations: Includes DistributedFileSystem, LocalFileSystem, FTPFileSystem, and WebHdfsFileSystem.
• URI and Path: Uses hdfs://host:port/location to access files, with configurations set in core-site.xml.

FSDataInputStream and FSDataOutputStream

• FSDataInputStream: Provides methods like seek() and getPos() for random access.
• FSDataOutputStream: Used for writing files, supports appending but not seeking.

YARN Overview

• Resource Management: YARN manages cluster resources, supporting various applications like Apache Hive and Storm.
• Scalability and Multi-Tenancy: Decouples resource management from job execution, allowing multiple applications to run concurrently.

YARN Architecture

• Components: Includes ResourceManager, ApplicationMaster, NodeManager, and Container.
• ResourceManager: Allocates resources and manages application scheduling.
• ApplicationMaster: Manages the lifecycle of applications, communicating with ResourceManager for resources.
• NodeManager: Manages node health and resources.

YARN Process Flow

• Task Execution: ApplicationMaster requests resources from ResourceManager, which allocates slots via NodeManager.
• Heartbeat Mechanism: Ensures continuous communication between components for task and resource management.

YARN Failures and High Availability

• Failure Management: Handles container, task, and ApplicationMaster failures with restarts and resource reallocation.
• High Availability: Uses ZooKeeper for failover management, ensuring ResourceManager redundancy with Active/Standby configurations.

This summary encapsulates the core functionalities and architecture of Hadoop’s HDFS and YARN, highlighting their operations, error handling, authentication methods, and resource management capabilities.

In a high-availability Hadoop cluster, only one ResourceManager is active while others remain in standby. The active ResourceManager syncs its state with standby ResourceManagers using a file-based or ZooKeeper-based state store. If the active ResourceManager becomes unresponsive, ZooKeeper elects a new active ResourceManager.

YARN’s ResourceManager comprises two main components: the ApplicationManager and the Scheduler. The ApplicationManager validates and forwards jobs to the Scheduler, which allocates resources based on policies like CapacityScheduler and FairScheduler. The Scheduler ensures fair resource distribution and prevents resource monopolization by longer jobs.

The Fair Scheduler allocates resources evenly, ensuring each job gets necessary slots while optimizing unused resources. Pools can have minimum resources guaranteed, and resource allocation is adjusted dynamically based on demand.

The Capacity Scheduler optimizes resources across organizations by guaranteeing capacity to each department and reallocating unused resources. It uses hierarchical queues with priority scheduling, ensuring resource limits per queue and enabling multi-tenancy, elasticity, and security.

YARN’s Application History Server tracks completed jobs, but it has limitations in scalability and reliability. The Application Timeline Server (ATS) addresses these by storing YARN application data in a time store, providing metric visibility and a robust query interface. ATS V2 improves scalability by using distributed timeline collectors and Apache HBase for storage, enhancing usability and supporting complex queries.

YARN Federation scales YARN infrastructure by subdividing large clusters into federated sub-clusters, each managed by its ResourceManager. This architecture ensures scalability, fault tolerance, and efficient resource management. A router abstracts the federated sub-clusters, directing job submissions based on policy and utilizing a state store for sub-cluster details.

MapReduce, a framework on Hadoop, processes large data sets in parallel. It divides data into key-value pairs processed by Mapper and Reducer tasks, enabling distributed data processing and aggregation across HDFS or AWS S3. The framework ensures reliability and cost-effectiveness by operating on common hardware.

MapReduce is a framework for processing large data sets with a distributed algorithm on a cluster. It operates in several key phases: mapping, shuffling, sorting, and reducing. The process begins with the Mapper, which transforms input data into key-value pairs. These pairs are then sorted and shuffled to ensure that all data with the same key is sent to the same Reducer.

Key Features of MapReduce

• Data Locality: Mapper tasks process data locally on the node where it resides, reducing network overhead and improving performance. Intermediate results are stored locally, not in HDFS, allowing for task re-execution on failure without data transfer.

• Parallel Processing: MapReduce supports parallel processing by distributing tasks across multiple nodes, allowing for efficient data handling and scalability.

• Loose Coupling: Mappers and Reducers operate independently, enabling them to run on separate nodes and minimizing inter-process dependencies.

• Scalability: The framework allows for easy scaling across clusters by adding more nodes, with minimal configuration changes needed.

• Fault Tolerance: The ApplicationMaster monitors tasks and reschedules failed ones without affecting overall job execution, ensuring robustness.

• Cost-Effectiveness: As an open-source framework, MapReduce runs on commodity hardware, avoiding the need for expensive storage solutions like RAID.

Phases in MapReduce

1. Mapper Phase: Converts input data into intermediate key-value pairs. The output can be further consolidated using a Combiner before being passed to the Reducer.

2. Buffering and Sorting: The Mapper output is stored in buffer memory and sorted by key. Once the buffer reaches a threshold, data is written to local disk.

3. Shuffling and Copying: Data is shuffled to ensure all similar keys are sent to the same Reducer. The Reducer copies this data using parallel threads to optimize processing time.

4. Merging: The Reducer merges sorted data into larger files, maintaining order, before final processing.

5. Reducing: The Reducer processes the merged data, aggregating values for each key, and writes the output to HDFS.

MapReduce Architecture

MapReduce operates on YARN (Yet Another Resource Negotiator), which manages resources across distributed frameworks. YARN consists of the ResourceManager and NodeManager. The ResourceManager allocates resources and manages application lifecycle through the ApplicationMaster, which coordinates tasks and handles failures.

Sample Program

A basic MapReduce program involves defining a Mapper to read inputs and produce key-value pairs, and a Reducer to aggregate these pairs. Configuration is set up in JobConf, specifying Mapper, Reducer, and input/output formats. The program is executed by packaging it into a JAR and running it with the Hadoop command line.

Composite Key Operations

For complex operations requiring multi-column processes, composite keys are used. These keys combine multiple fields, such as “country” and “state,” and implement the WritableComparable interface to facilitate sorting and serialization. This approach allows for efficient grouping and processing based on multiple criteria.

Overall, MapReduce provides a robust, scalable, and efficient way to process large datasets in a distributed computing environment, leveraging parallelism and fault tolerance to handle complex data operations.

The text provides an in-depth overview of MapReduce and Hive, two critical components in big data processing using Hadoop.

MapReduce:

1. Mapper and Reducer Workflow:

   • MapReduce processes data in key-value pairs using the WritableComparable and Writable interfaces for sorting and writing data.
   • The Mapper class processes input splits and outputs intermediate data, which is grouped and sorted by key.
   • A Combiner can be used to reduce data transfer between the Mapper and the Reducer.
   • The Reducer merges, shuffles, and sorts data before processing collections of values for each key.

2. Job Configuration:

   • Configured using JobConf, which sets parameters like Mapper, Reducer, and Input/Output formats.
   • The job is executed by packaging it into a JAR file and running it via the command line.

3. Performance Tuning:

   • Key parameters in mapred-site.xml optimize performance, including buffer memory (mapreduce.task.io.sort.mb), number of Reducer tasks (mapreduce.job.reduces), and shuffle settings (mapreduce.reduce.shuffle.parallelcopies).

Hive:

1. Overview:

   • Apache Hive is a framework built on Hadoop for big data analysis using a SQL-like language, HiveQL.
   • Hive abstracts SQL to access Hadoop data, translating HiveQL into MapReduce code.

2. Components and Access:

   • Hive can be accessed through various interfaces like CLI, JDBC, and ODBC.
   • It supports different execution engines, including MapReduce, Tez, and Spark.

3. Data Storage and Formats:

   • Hive stores data in tables mapped to HDFS directories, with partitions and buckets for organization.
   • Supports multiple file formats like Text, Sequence, RCFile, Avro, ORC, and Parquet.

4. Data Types:

   • Supports primitive types (integers, floats, strings) and complex types (arrays, maps, structs).
   • Data types inspired by Java, facilitating ease of use for developers familiar with Java.

5. History and Adoption:

   • Developed by Facebook to simplify data processing with Hadoop, addressing the complexity of MapReduce.
   • Widely adopted by organizations like Amazon, IBM, and Netflix.

Hive significantly eases the process of querying and managing large datasets in Hadoop, offering a more user-friendly alternative to writing complex MapReduce programs.

The text provides an overview of data types, Hive query language (HiveQL), complex data types, data definition language (DDL), table structures, partitioning, bucketing, and Hive architecture.

Data Types

• Primitive Types: Include 8-byte signed integers, 4-byte floats, 8-byte doubles, strings, and booleans.
• Timestamps: Can be interpreted as UNIX timestamps or in JDBC-compliant formats.
• Complex Types: Include arrays, maps, structs, and unions. These are built on primitive types and support nested structures.

HiveQL

HiveQL is similar to SQL but tailored for big data processing. It differs from SQL by not supporting insert, update, or delete operations to favor performance. Key operations include creating, inserting, and managing tables and databases.

Data Definition Language (DDL)

• Database Operations: Create, show, describe, and drop databases.
• Table Operations: Create, alter, drop, and manage tables, including partitioning and external tables.

Table Structures

Hive organizes HDFS data into tables, with support for both primitive and complex types. Tables can be partitioned to optimize query performance by reducing the data scan scope.

Partitioning

Partitioning divides data into slices, creating sub-directories for each partition, optimizing query performance by limiting data scans to relevant partitions.

Bucketing

Bucketing further divides data within partitions using hash functions, improving query performance and enabling efficient data sampling and analysis.

Hive Architecture

Hive runs atop Hadoop, translating HiveQL into execution plans:

• Components:
  • Hive Client: Interfaces with external tools.
  • Metastore: Stores metadata about tables and partitions.
  • Thrift Server: Enables API access for different languages.
  • Driver: Manages query execution.
  • Compiler: Parses HiveQL and interacts with Metastore.
  • Execution Engine: Executes tasks on Hadoop.

Serialization/Deserialization (SerDe)

Hive uses SerDe to read/write data in HDFS. Users can implement custom SerDe for specific data formats.

Metastore

Stores metadata information using an RDBMS and facilitates metadata access for query processing.

Query Compiler

The compiler parses HiveQL, generates execution plans, and optimizes query execution through transformations and rule applications.

HiveServer2

HiveServer2 supports concurrent requests and secure authentication, using a Thrift-based RPC call and JDBC driver for executing Hive queries.

Hive Setup

The setup involves installing Java, Hadoop, and Hive, configuring environment variables, and ensuring prerequisites like Cygwin for Windows.

This summary provides a concise yet comprehensive overview of Hive’s functionality and setup, focusing on its capabilities in managing and querying large datasets efficiently.

Hive Basics

To start using Hive, ensure Hadoop is in your class path (export HADOOP_HOME=<hadoop-install-dir>). Create necessary HDFS directories with appropriate permissions:

bash $HADOO{P}_{H}OME/bin/hadoopfs-mkdir/tmp$HADOOP_HOME/bin/hadoop fs -mkdir /user/hive/warehouse $HADOO{P}_{H}OME/bin/hadoopfs-chmodg+w/tmp$HADOOP_HOME/bin/hadoop fs -chmod g+w /user/hive/warehouse

Launch Hive using the command % hive. The default database is “default”, and HiveQL is case-insensitive. Tables can be managed (data is deleted when the table is dropped) or external (data remains when the table is dropped).

Table Creation and Data Loading

Create tables using HiveQL:

sql CREATE EXTERNAL TABLE IF NOT EXISTS employee ( EMPID STRING, EMPNAME STRING ) ROW FORMAT DELIMITED FIELDS TERMINATED BY ’,’ LINES TERMINATED BY ‘\n’ LOCATION ‘/user/joe/employee’;

Load data from local files into Hive tables:

sql LOAD DATA LOCAL INPATH ‘filepath’ INTO TABLE tablename;

Hive Configuration

Configure Hive using hive-site.xml or dynamically via CLI:

• Execution Engine: Set with hive.execution.engine (default: mr, can be tez or spark).
• Reducers: Controlled via hive.exec.reducers.bytes.per.reducer (default: 256 MB).
• Parallel Execution: Enable with hive.exec.parallel=true.
• Limit Optimization: Use hive.limit.optimize.enable=true to optimize queries with limits.

Transactions and ACID Support

Hive supports ACID transactions (Atomicity, Consistency, Isolation, Durability) for managed tables stored in ORC format. Transactions are enabled by setting:

• Client Side:
  • hive.support.concurrency=true
  • hive.txn.manager=org.apache.hadoop.hive.ql.lockmgr.DbTxnManager
• Server Side:
  • hive.compactor.initiator.on=true

Data Manipulation

Insert data using INSERT INTO or INSERT OVERWRITE for replacing existing data. Dynamic partitions can be handled by enabling hive.exec.dynamic.partition.

• Update: Modify data with UPDATE statements.
• Delete: Remove data using DELETE.
• Merge: Combine INSERT, UPDATE, and DELETE using MERGE.

Locks

Hive uses locks to manage concurrency, with shared (S) and exclusive (X) locks for reading and modifying data, respectively. Lock management is handled by the Lock Manager, which uses heartbeats to manage stale locks.

Queries

Use HiveQL for data retrieval:

sql SELECT [ALL | DISTINCT] select_expr FROM table_reference [WHERE where_condition] [GROUP BY col_list] [ORDER BY col_list] [CLUSTER BY col_list | DISTRIBUTE BY col_list | SORT BY col_list] [LIMIT [offset,] rows]

This overview provides the essential commands and configurations to effectively utilize Hive for big data processing with Hadoop.

Hive Query Language (HiveQL) offers SQL-like syntax for querying data stored in Hadoop. It supports operations like SELECT, WHERE, DISTINCT, LIMIT, and more, similar to RDBMS. Hive queries can be run directly or from files using the -f option. Hive supports complex data types such as arrays, maps, and structs, and can handle JSON data.

SELECT and WHERE Clauses:

• SELECT is used to specify columns, and WHERE filters results based on conditions.
• DISTINCT removes duplicate rows, and LIMIT restricts the number of returned records.

Complex Data Types:

• Arrays: Ordered collections accessed with square brackets.
  • Example: SELECT emails[0] FROM employee;
• Maps: Unordered key-value pairs.
  • Example: SELECT previous_company["comp1"] FROM employee;
• Structs: Collections of different data types.
  • Example: SELECT address.city FROM employee;

Sorting and Distribution:

• ORDER BY sorts all data using a single reducer, which can be slow for large datasets.
• SORT BY sorts data within each reducer, improving performance.
• DISTRIBUTE BY distributes rows based on column values, ensuring non-overlapping data ranges across reducers.
• CLUSTER BY combines DISTRIBUTE BY and SORT BY for global ordering.

Grouping and Aggregation:

• GROUP BY groups data for aggregation functions like SUM, AVG, COUNT, MIN, and MAX.
• HAVING filters groups based on conditions.
• Hive supports advanced aggregation functions like GROUPING SETS, CUBE, and ROLLUP.

Table-Generating Functions:

• Functions like EXPLODE expand arrays or maps into multiple rows.
• Example: SELECT explode(emails) AS email FROM employee;

Utility Functions:

• Mathematical: Functions like ROUND, FLOOR, CEIL, and SQRT.
• Collection: Functions like SIZE, MAP_KEYS, and ARRAY_CONTAINS.
• Date: Functions like FROM_UNIXTIME, UNIX_TIMESTAMP, YEAR, MONTH, DAY.
• Conditional: Functions like IF, ISNULL, NVL, and COALESCE.
• String: Functions like CONCAT, LENGTH, UPPER, LOWER, TRIM.

Join Operations:

• Hive supports various joins, such as inner and left outer joins, allowing complex data retrieval without manual MapReduce coding.
• Example of inner join: SELECT * FROM tableA JOIN tableB ON tableA.id = tableB.id;

HiveQL’s rich set of functions and constructs enable efficient data manipulation and analysis in Hadoop environments, leveraging SQL-like syntax for ease of use. The language’s ability to handle complex data types and perform advanced aggregations and joins makes it a powerful tool for big data processing.

Hive Joins

• Right Outer Join: Retains all records from the right table and fills missing fields from the left table with NULLs.
• Left Semi Join: Used instead of IN for subqueries, as Hive does not support IN EXISTS. Example: SELECT emp.* FROM employee emp LEFT SEMI JOIN address add ON emp.empid=add.empid;.
• Map Side Join: Optimizes join queries by loading smaller tables into memory first, improving performance. Deprecated hints like MAPJOIN are replaced by settings such as hive.auto.convert.join=true.

Hive Union

• UNION ALL: Combines subqueries with the same number of columns and types, useful for simplifying complex queries.

Hadoop File Formats

• Columnar Format: Stores data by columns, improving I/O efficiency and query performance by only retrieving necessary columns.
• Row Format: Reads all columns, impacting performance negatively in wide tables.

File Format Characteristics

• Optimized Read: Columnar formats allow efficient data retrieval by grouping column values.
• Optimized Compression: Columnar formats compress data of similar types together, enhancing storage efficiency.

Schema Evolution

• Supports backward compatibility, allowing schema changes without rewriting existing data. This is crucial for handling large datasets.

Splittable Formats

• Important for distributing data across clusters. Formats like JSON and XML are not splittable, impacting performance.

Compression

• Compression Benefits: Reduces storage costs and improves data transfer rates. Formats like .gzip and .bzip2 vary in compression levels and splittability.

Specific File Formats

• Text: Simple, line-based, supports file-level compression. Slow for read/write operations.
• Sequence: Combines small files into larger ones, optimizing HDFS storage. Supports both block and record compression.
• Avro: Language-neutral, supports schema reconciliation, and offers rich data type support. Uses JSON for schema definition and is suitable for dynamic schema changes.

Avro Schema Example

• Defines schema using JSON with fields like type, namespace, name, fields, and default. Supports language portability and cross-language interoperability.

Key Features of Avro

• Language Portability: Embedded schema makes it independent of programming languages.
• Cross-Language Interoperability: JSON-based schema allows interaction across different languages.
• Schema Evolution: Handles changes like adding/removing columns without affecting existing data.

This summary provides a concise overview of Hive joins, Hadoop file formats, and the characteristics and benefits of specific formats like Avro, emphasizing their role in optimizing data processing and storage in big data environments.

Avro provides schema evolution, allowing backward compatibility with older schemas. It supports splittable and compressed file formats, which are essential for efficient performance and storage in Hadoop. The code example demonstrates writing and reading Avro files, creating a schema, and integrating Avro with Hive tables as external tables. Avro is a row-oriented format beneficial for ETL operations and supports schema evolution, making it suitable for frequent schema changes.

RCFile (Record Columnar File) partitions records into row and column groups, optimizing storage and query processing. It offers high compression and column-oriented storage, making it efficient for queries on selected columns. However, data needs to be loaded into a temporary table before overwriting into an RCFile table.

ORC (Optimized Record Columnar) format provides efficient reading, writing, and processing by storing data in a columnar format. It offers better compression and faster queries than RCFile, with built-in indexing and support for schema evolution. ORC files consist of stripes, which include data, index, and footer, and support concurrent reads and Hive’s complex data types.

Parquet is a columnar file format optimized for Hadoop, supporting nested data structures and efficient storage. It allows reading specific columns without accessing entire records, making it ideal for wide tables with many columns. Parquet uses a record shredding and assembly algorithm for storing nested structures and supports both file-level and block-level compression.

Comparatively, Avro is row-oriented and excels in fast reading and writing with schema evolution support, while Parquet is columnar, offering better read performance and optimized storage for a subset of columns. ORC is similar to Parquet but performs better with flattened data and supports ACID transactions. Both ORC and Parquet are suited for analytics and queries on large datasets.

Hadoop uses data compression to reduce storage space and improve processing time by minimizing data transfer across the network. Compression is beneficial for large datasets, reducing I/O and network bottlenecks. Block compression in Hadoop allows parallel processing of data blocks, enhancing performance in distributed environments.

Overall, choosing the right file format and compression strategy in Hadoop depends on the specific use case, data structure, and processing requirements. Avro is ideal for schema evolution and ETL, Parquet for columnar data access, and ORC for optimized query performance and ACID support.

In Hadoop, data compression is crucial for optimizing storage and performance. Hadoop MapReduce automatically decompresses data during processing. If compression is splittable, it enhances performance by allowing data to be distributed across blocks, facilitating parallel processing. Intermediate compression reduces temporary storage needs and improves I/O rates, while output compression optimizes HDFS storage and access.

Compression codecs in Hadoop, such as Gzip, Bzip2, LZO, and Snappy, offer varying trade-offs between compression ratio, speed, and splittability. Gzip provides a high compression ratio but is non-splittable, making it less suitable for MapReduce. Bzip2 offers higher compression and is splittable, but it’s slower and CPU-intensive. LZO strikes a balance with moderate compression and fast processing, and Snappy, known for speed, is splittable but offers lower compression.

Choosing the right compression depends on use cases, balancing speed and storage. Splittable formats are preferable for large files to enable parallel processing. Non-splittable formats like Gzip can degrade performance by requiring all data to be processed by a single task. Benchmarking different strategies with representative datasets is recommended to find the optimal solution.

Hadoop’s architecture includes HDFS, which uses data replication and erasure coding for fault tolerance and efficiency. HDFS federation and high availability features enhance scalability and reliability. YARN, the resource manager, supports diverse data processing frameworks, offering features like high availability and federation for large-scale deployments.

Hive, built on Hadoop, provides SQL-like querying capabilities. It supports various data types and file formats, including Avro and ORC, which offer benefits like schema evolution and efficient data storage. Hive’s architecture includes a Metastore for metadata management and HiveServer2 for client interactions. It supports complex queries, joins, and data manipulation operations, making it suitable for big data analytics.

Overall, Hadoop’s ecosystem, including tools like Hive, Pig, and Sqoop, provides a robust framework for processing and analyzing large datasets, with compression playing a key role in optimizing performance and storage.