Introduction to Redis Data Structures: Hashes

Jun 24, 2016Download as pptx, pdf

1 like437 views

Learn about Redis data structures: hashes and contact us for hassle-free hosting for mongodb® and Redis® Retrieve your connection string and start using your cluster.

Hashes
Introduction to Redis Data Structures:

What is Redis?
Open Source, NoSQL Database
Used by: Twitter, Pinterest, GitHub
Stores Advanced Data Structures
Client Support: Java, C, Node.js etc
Introduction to Redis Data Structures: Hashes

What are Hashes?
Hashes that map string names to string values
They are essentially named containers of unique fields and their values
A way to represent an object as a Redis data structure
They also provide constant time basic operations like get, set, exists, etc.
Introduction to Redis Data Structures: Hashes

Common Use Cases for Hashes
Suited to store objects: sessions, users,
visitors etc.
Makes it one of key data structures provided
by Redis
In its memory optimized form, it is an
excellent choice for caching large amounts of
data
Introduction to Redis Data Structures: Hashes

Hashes Operations in Redis
HKEYS HLEN
HGET HMGET
HEXISTS HGETALL
HDEL HINCRBY
The complete list of set related Redis commands can be found here.
Introduction to Redis Data Structures: Hashes

Internal Implementation
Implemented as hash tables that use the
hash function MurmurHash2
Grow via incremental resizing
Hashes with few keys can be packed
cleverly into linear array like structure
Introduction to Redis Data Structures: Hashes

Redis Sets vs Redis Hashes
Implemented as dictionaries
Storage optimization made for
smaller hashes
Provide constant time basic
operations
Introduction to Redis Data Structures: Hashes
Ziplist is used to optimize storage
of smaller sorted sets and lists

Summary
One of the well known recommendations for memory savings while using Redis
is to use hashes instead of plain strings
Small hashes are encoded in a very small space, so you should try representing
your data using hashes every time it is possible
Redis provides fairly useful and advanced operations on hashes
Introduction to Redis Data Structures: Hashes

This document provides information about a Big Data and Hadoop training course. The course covers topics like introduction to Big Data and Hadoop, Hadoop fundamentals including HDFS, Hadoop computing using MapReduce and YARN, and data processing using tools like Pig, Hive, Spark, and Flume. It also covers data management tools like HBase and Sqoop. The training aims to help students understand how to perform analytical operations to gain insights from data using Hadoop and make them proficient in handling both structured and unstructured data.

Big data and hadoopPrashanth Yennampelli

This document discusses big data and the Apache Hadoop framework. It defines big data as large, complex datasets that are difficult to process using traditional tools. Hadoop is an open-source framework for distributed storage and processing of big data across commodity hardware. It has two main components - the Hadoop Distributed File System (HDFS) for storage, and MapReduce for processing. HDFS stores data across clusters of machines with redundancy, while MapReduce splits tasks across processors and handles shuffling and sorting of data. Hadoop allows cost-effective processing of large, diverse datasets and has become a standard for big data.

Real-time analytics with HBaseData Science Society

Mongo dbSTIinnsbruck

This document discusses NoSQL databases and MongoDB. It describes the different types of NoSQL databases, including key-value stores, column family stores, document databases and graph databases. It then provides details about MongoDB, including that it is a document-oriented NoSQL database that is schema-free and uses collections to organize data. It explains basic CRUD operations in MongoDB like create, read, update and delete. It concludes with details about MongoDB's funding and number of employees.

The future of Big Data toolingData Science Society

Alexander Aldev - Co-founder and CTO of MammothDB, currently focused on the architecture of the distributed database engine. Notable achievements in the past include managing the launch of the first triple-play cable service in Bulgaria and designing the architecture and interfaces from legacy systems of DHL Global Forwarding's data warehouse. Has lectured on Hadoop at AUBG and MTel. "The future of Big Data tooling" will briefly review the architectural concepts of current Big Data tools like Hadoop and Spark. It will make the argument, from the perspective of both technology and economics, that the future of Big Data tools is in optimizing local storage and compute efficiency.

Redis - Your Magical superfast databasethe100rabh

Redis is an in-memory database that provides fast response times for ad serving traffic. It is used to search for the right ad based on a user's location and profile from over 100 million profiles. Redis allows for key-value storage with support for different data types like strings, lists, sets, hashes, and sorted sets. It can be configured for in-memory storage with disk persistence, replication across multiple slaves, or horizontal and vertical sharding. At Adnear, Redis is configured with master-slave replication across nodes to support different ad engines with sub-second response times for over 250 million daily requests.

Introduction to Big DataMd. Afif Al Mamun

Big data refers to large volumes of structured and unstructured data that can be analyzed to reveal patterns and trends. It is characterized by 3 Vs - volume, velocity, and variety. Hadoop and associated tools like HDFS, MapReduce, Hive and NoSQL databases are used to handle big data. These tools provide scalability, flexibility and support both structured and unstructured data. Understanding big data analytics provides opportunities in data science and IT jobs and benefits industries like banking, healthcare, manufacturing and more through real-time insights.

Cassandra one pageMilind Zodge

First Step in NoSqlDelwar Hossain

Introduction To MongoDBYubraj Ghimire

What does the future of Big data look like?How to get a fresher job in data a...Acutesoft Solutions India Pvt Ltd

Dataspace presentatieRoland Cornelissen

«NoSQL Databases and Polyglot Persistence»Olga Lavrentieva

This document discusses polyglot persistence, which is using multiple data storage technologies chosen based on how applications use data. It covers NoSQL databases including key-value stores, column-family stores, document databases and graph databases. Common polyglot persistence patterns are described like using a relational database for transactions, a key-value store for sessions, and a document database for product catalogs. The challenges of a science warehouse project are presented, and using a polyglot approach with NoSQL databases for logging and price history is proposed as a solution.

NoSQL DatabasesEduard Tudenhoefner

This document provides an overview of NoSQL databases, including why they were created, common characteristics, and classifications. It discusses key concepts like the CAP theorem, BASE vs ACID properties, and gives examples like Cassandra. Cassandra is a distributed, horizontally scalable database designed for high availability. It uses consistent hashing to distribute data and is very fast for writes. The document concludes with tradeoffs between SQL and NoSQL databases and when each may be preferable.

NoSQL DatabasesAshish Karki

NoSQL databases are designed for large data storage needs in distributed databases. Unlike SQL databases, NoSQL databases do not use SQL for queries and are schema-free, horizontally scalable, and fault tolerant. The main categories of NoSQL databases are document oriented, XML based, graph databases, and key-value stores. Major tech companies like Google, Facebook, and Amazon use NoSQL databases like BigTable, Cassandra, and DynamoDB.

Clustering in Data MiningS.Mostafa Sayyedi

This document summarizes a group project report on clustering in data mining. It discusses different types of clustering algorithms including K-means clustering and agglomerative hierarchical clustering. For K-means clustering, it provides an example showing how clusters are formed by assigning objects to centroids and recalculating centroids over iterations. For hierarchical clustering, it shows how clusters are merged based on distances recorded in a distance matrix and represented through a dendrogram. The document contains examples and diagrams to illustrate key steps and concepts in clustering techniques.

Deep dive into druidOfir Nir

This document provides an overview of Druid, an open-source analytics database. It discusses Druid's column-oriented architecture and ability to handle real-time and historical data across a distributed cluster. The document outlines key Druid concepts, the indexing and query processes, and provides some benchmark numbers from a production Druid cluster. It aims to introduce the reader to Druid's capabilities for optimized analytics querying and horizontal scalability.

Big Data Introductionyalla4u

This document discusses big data and Hadoop. It defines big data as very large and complex datasets that are difficult to process using traditional methods. It then lists some of the challenges of big data like storage, processing speed, and cost. The document introduces Hadoop as an open-source framework that can address these challenges by allowing distributed storage and processing of large datasets across commodity hardware. It provides a high-level overview of how Hadoop works, including its core components HDFS for storage and MapReduce for distributed processing.

Theory of LaTeXSrikrishnan Suresh

LaTeX is a document preparation system that separates document styling from content using markup language. It applies a design principle of separating what changes (content in a .tex file) from what remains the same (styling information in a .cls file). This avoids rework and makes LaTeX suitable for complex, large documents since the focus is on content over formatting. LaTeX results in better typography than other systems, with fewer hyphenations, less variation in word and line spacing, and no overly long lines of text.

H baseShashwat Shriparv

HBase Essentialsueakymxzz

This 164-page book published in 2014 provides an introduction to the scalable HBase database and is intended for developers and engineers looking to learn about HBase. The book assumes some familiarity with HDFS and MapReduce and is aimed at those interested in exploring HBase as a NoSQL database solution. It provides hands-on instruction for getting started with HBase's architecture and basic operations.

10 mongo dbAhmed Elbassel

XDC demo: CTAEOSC-hub project

This document discusses a CTA data workflow use case for the eXtreme DataCloud project. The goals are to archive petabytes of data using an ingest method, index files with metadata from header files, and query files using metadata parameters. Key requirements include using OneData services for metadata management and quality of service policies. The document describes how an archive manager can configure rules for archiving, duplicating, and preventing file deletions based on metadata. It also describes how an archive user can query for file names based on metadata parameters. Finally, it provides an overview of the use case architecture and components demonstrated, including metadata indexing, data ingestion, and file querying and retrieval.

4th OpenAIRE Workshop, Birgit SchmidtOpenAIRE

INTRODUCTION OF BIG DATAHarshitChaurasia6

This document introduces big data and provides an overview of key concepts. Big data refers to large, complex datasets that cannot be processed by traditional software. It is characterized by volume, velocity, variety, and veracity. Hadoop is an open-source framework for storing and processing big data across clusters of computers using MapReduce. Hive provides a data warehouse infrastructure to process structured data in Hadoop, while MapReduce is a programming model for parallel processing of large datasets.

Hadoop Training Tutorial for Freshersrajkamaltibacademy

Docker & Diego - good friends or not? | anyninesanynines GmbH

Diego & Docker can work together but their friendship has issues. Diego allows Cloud Foundry to run Docker containers by treating Docker containers as Garden containers. However, using both together adds complexity since developers must build Docker images, push them to a registry, and deploy each Dockerized app separately to Cloud Foundry rather than with a single command. While together they provide deployment options, simplifying the process would improve their relationship.

OpenCms Days 2016: Next generation content repository Alkacon Software GmbH & Co. KG

All OpenCms versions so far have relied on a relational SQL database as the backbone of the content repository. However, new requirements are emerging, such as distributed repositories with automatic installation in the cloud, which are difficult to meet using the existing database infrastructure. Alkacon has been investigating the possibility to switch to a new repository technology for future OpenCms versions that come after 10.5 for about a year now. The new repository should be based on state-of-the-art open source components and provide much better cloud and clustering support. In recent months, Alkacon has started with the implementation of this next generation repository. First results are in, and they look promising.

More Related Content

What's hot (20)

Introduction to Big DataMd. Afif Al Mamun

Cassandra one pageMilind Zodge

First Step in NoSqlDelwar Hossain

Introduction To MongoDBYubraj Ghimire

What does the future of Big data look like?How to get a fresher job in data a...Acutesoft Solutions India Pvt Ltd

Dataspace presentatieRoland Cornelissen

«NoSQL Databases and Polyglot Persistence»Olga Lavrentieva

NoSQL DatabasesEduard Tudenhoefner

NoSQL DatabasesAshish Karki

Clustering in Data MiningS.Mostafa Sayyedi

Deep dive into druidOfir Nir

Big Data Introductionyalla4u

Theory of LaTeXSrikrishnan Suresh

H baseShashwat Shriparv

HBase Essentialsueakymxzz

10 mongo dbAhmed Elbassel

XDC demo: CTAEOSC-hub project

4th OpenAIRE Workshop, Birgit SchmidtOpenAIRE

INTRODUCTION OF BIG DATAHarshitChaurasia6

Hadoop Training Tutorial for Freshersrajkamaltibacademy

Introduction to Big DataMd. Afif Al Mamun

Cassandra one pageMilind Zodge

First Step in NoSqlDelwar Hossain

Introduction To MongoDBYubraj Ghimire

What does the future of Big data look like?How to get a fresher job in data a...Acutesoft Solutions India Pvt Ltd

Dataspace presentatieRoland Cornelissen

«NoSQL Databases and Polyglot Persistence»Olga Lavrentieva

NoSQL DatabasesEduard Tudenhoefner

NoSQL DatabasesAshish Karki

Clustering in Data MiningS.Mostafa Sayyedi

Deep dive into druidOfir Nir

Big Data Introductionyalla4u

Theory of LaTeXSrikrishnan Suresh

H baseShashwat Shriparv

HBase Essentialsueakymxzz

10 mongo dbAhmed Elbassel

XDC demo: CTAEOSC-hub project

4th OpenAIRE Workshop, Birgit SchmidtOpenAIRE

INTRODUCTION OF BIG DATAHarshitChaurasia6

Hadoop Training Tutorial for Freshersrajkamaltibacademy

Viewers also liked (10)

Docker & Diego - good friends or not? | anyninesanynines GmbH

OpenCms Days 2016: Next generation content repository Alkacon Software GmbH & Co. KG

NoSQL - MongoDBJuan Azcurra

How To Connect Spark To Your Own DatasourceMongoDB

1) Ross Lawley presented on connecting Spark to MongoDB. The MongoDB Spark connector started as an intern project in 2015 and was officially launched in 2016, written in Scala with Python and R support. 2) To read data from MongoDB, the connector partitions the collection, optionally using preferred shard locations for locality. It computes each partition's data as an iterator to be consumed by Spark. 3) For writing data, the connector groups data into batches by partition and inserts into MongoDB collections. DataFrames/Datasets will upsert if there is an ID. 4) The connector supports structured data in Spark by inferring schemas, creating relations, and allowing multi-language access from Scala, Python and R

MongoDB Launchpad 2016: Moving Cybersecurity to the CloudMongoDB

Timothy Keeler discussed moving SecureONE, a security product that allows just-in-time administration and access controls, from an on-premise to cloud architecture using MongoDB Atlas. The cloud architecture provides multi-tenant capabilities and scales automatically. MongoDB Atlas simplified the migration by handling infrastructure maintenance, backups, high availability, and automatic scaling with no need to design the database architecture. This saved development time and reduced costs versus maintaining an on-premise database cluster.

MongoDB Launchpad 2016: What’s New in the 3.4 ServerMongoDB

Asya Kamsky, a lead product manager at MongoDB, discussed improvements, extensions, and innovations in MongoDB. These included improvements to the Wired Tiger storage engine, replica set election process, and initial sync process. MongoDB was also extended with features like document validation, partial indexes, $lookup, read-only views, and faceted search. Innovations involved improvements to the aggregation pipeline, mixed storage engine sets, zones, and BI connectors.

MongoDB Launchpad 2016: MongoDB 3.4: Your Database EvolvedMongoDB

MongoDB 3.4 introduces new features that make it ready for mission-critical applications, including stronger security, broader platform support, and zones. It provides multiple data models in a single database, including document, graph, key-value, and search. Modernized tooling offers powerful capabilities for data analysts, DBAs, and operations teams. Key features of 3.4 include zones for geographic distribution, LDAP authorization, elastic clusters for scalability without disruption, and tunable consistency options.

Python Ireland Conference 2016 - Python and MongoDB WorkshopJoe Drumgoole

This document provides an agenda and overview for a MongoDB and Python workshop. It begins with an introduction to NoSQL databases and MongoDB. It then covers how to install and run MongoDB, create a simple blog application to demonstrate working with MongoDB documents, and how to efficiently create large amounts of sample data. The document is intended to guide attendees through hands-on examples for getting started with MongoDB and developing applications using Python.

MongoDB and BlockChainMassimo Brignoli

Blockchains promise significant benefits but also face challenges to widespread adoption. Some key challenges are a lack of interoperability between different blockchain networks and platforms, as well as issues around integration with existing systems and scaling to meet enterprise needs. Interledger aims to address the interoperability challenge by providing a protocol for connecting various ledger systems, including blockchains, to enable asset transfers between them. This allows for a more connected "Internet of Value" and new business opportunities across platforms.

Spark Summit EU talk by Ross LawleySpark Summit

Docker & Diego - good friends or not? | anyninesanynines GmbH

OpenCms Days 2016: Next generation content repository Alkacon Software GmbH & Co. KG

NoSQL - MongoDBJuan Azcurra

How To Connect Spark To Your Own DatasourceMongoDB

MongoDB Launchpad 2016: Moving Cybersecurity to the CloudMongoDB

MongoDB Launchpad 2016: What’s New in the 3.4 ServerMongoDB

MongoDB Launchpad 2016: MongoDB 3.4: Your Database EvolvedMongoDB

Python Ireland Conference 2016 - Python and MongoDB WorkshopJoe Drumgoole

MongoDB and BlockChainMassimo Brignoli

Spark Summit EU talk by Ross LawleySpark Summit

Similar to Introduction to Redis Data Structures: Hashes (20)

Use Redis in Odd and Unusual WaysItamar Haber

Redis basicsArthur Shvetsov

Redis data structure and Performance OptimizationKnoldus Inc.

Redis Workshop on Data Structures, Commands, AdministrationHashedIn Technologies

An Introduction to Redis for .NET Developers.pdfStephen Lorello

Redis is an in-memory data structure store that can be used as a database, cache, or message broker. It supports various data structures like strings, hashes, lists, sets, sorted sets, streams and geospatial indexes. Redis is single-threaded, runs on a single server, and is very fast. It provides different deployment modes like standalone, sentinel-based replication for high availability, and clustering for horizontal scaling. The StackExchange.Redis client is recommended for .NET developers to connect and interact with Redis.

Redis introductionFederico Daniel Colombo Gennarelli

Redis is a key-value store that can be used as a database, cache, or message broker. It supports string, hash, list, set and sorted set data structures. Redis is written in C and works on most POSIX systems. It is single-threaded but can scale horizontally by running multiple Redis instances. Redis can persist data to disk and support master-slave replication. It can be scaled out using sentinel for automatic failover, Twemproxy for data sharding, or Redis Cluster for sharding with automatic failover.

Redis databaseÑáwrás Ñzár

Redis is an in-memory data structure store that can be used as a database, cache, or message broker. It supports various data structures like strings, hashes, lists, sets, and sorted sets. Data can be persisted to disk for durability and replicated across multiple servers for high availability. Redis also implements features like expiration of keys, master-slave replication, clustering, and bloom filters.

Redis Functions, Data Structures for Web Scale AppsDave Nielsen

An Introduction to Redis for Developers.pdfStephen Lorello

Redis is a popular, powerful, and—most of all—FAST database. Developers worldwide use Redis as a Cache, a session store, a message bus, and even as their regular database. In this session, we'll introduce Redis, and learn some of the key design patterns that you can use with Redis. Then we'll go over how Redis fits into the ecosystem, and how to use it in our applications—including some of the major gotchas to avoid.

Redis overviewAhmad El-khuja

Redis is an in-memory key-value data store that is commonly used for caching, messaging queues, and storing short-lived data. It stores data in RAM for fast read/write speeds and supports various data structures like strings, lists, sets, sorted sets, and hashes. Redis persists data either through snapshots stored on disk periodically or by logging all write operations. It is widely adopted by major tech companies due to its speed, rich data types, and atomic operations.

Redis Modules - Redis India Tour - 2017HashedIn Technologies

The document provides an overview of Redis Modules, which allow Redis to be extended through dynamically loaded libraries written in C. Some key modules discussed include ReJSON for storing and querying JSON documents natively in Redis, RediSearch for full-text search capabilities, and ReBloom for implementing scalable Bloom filters. Redis Modules can be used to add new data types, commands, and capabilities to Redis in order to adapt it to specific use cases and data models. Performance benchmarks show modules like ReJSON providing significant performance advantages over alternatives that rely on Redis' core data structures and Lua scripting.

Introduction to redisTanu Siwag

This document provides an overview and introduction to Redis, including: - Redis is an open source, in-memory data structure store that can be used as a database, cache, and message broker. - It supports common data structures like strings, hashes, lists, sets, sorted sets with operations like GET, SET, LPUSH, SADD. - Redis has advantages like speed, rich feature set, replication, and persistence to disk. - The document outlines how to install and use Redis, and covers additional features like pub/sub, transactions, security and backup.

Redis — The AK-47 of Post-relational DatabasesKarel Minarik

Redis is described as the "AK-47 of post-relational databases" due to its simplicity, reliability, and versatility. It provides data structures like strings, lists, sets, sorted sets and hashes that allow for features like caching, sessions, pub/sub, and more. Operations are fast due to operating entirely in memory. Redis is simple to install, learn, and use for a wide range of common data services and aggregation tasks.

Redis. Seattle Data Science and Data Engineering MeetupAbhishek Goswami

2_Redis.pptx very useful for begginers yessoufianbouktaib1

Introduction to RedisDvir Volk

Florida Man Uses Cache as Database.pdfStephen Lorello

Steve Lorello presented on Redis, dispelling common myths and demonstrating how to store and query complex objects. The key points covered included: 1. Redis can provide ACID transactions through individual commands and scripting. Data is always consistent on a single instance and eventually consistent with replication. 2. Redis ensures durability through append-only files and snapshotting databases to disk. 3. Complex objects can be stored in Redis as hashes, structured blobs, or JSON for flexible querying. 4. Secondary indexes like sorted sets or RediSearch enable finding objects by values through queries. 5. Redis OM and LINQ simplify .NET applications by providing declarative indexes and query capabilities.

10 Ways to Scale Your Website Silicon Valley Code Camp 2019Dave Nielsen

Redis has 10 different data structures (String, Hash, List, Set, Sorted Set, Bit Array, Bit Field, Hyperloglog, Geospatial Index, Streams) plus Pub/Sub and many Redis Modules. In this talk, Dave will give 10 examples of how to use these data structures to scale your website. I will start with the basics, such as a cache and User session management. Then I demonstrate user generated tags, leaderboards and counting things with hyberloglog. I will with a demo of Redis Pub/Sub vs Redis Streams which can be used to scale your Microservices-based architecture.

Redis/Lessons learnedTit Petric

redis-demo.pptxSNEHKUMAR30

Redis is an in-memory data structure store that can be used as a database, cache, or message broker. It supports various data types like strings, hashes, lists, and sets. Data can be persisted to disk for durability. Redis guarantees consistency through append-only files and availability through master-slave replication and clustering, where data is automatically distributed across multiple nodes.

Use Redis in Odd and Unusual WaysItamar Haber

Redis basicsArthur Shvetsov

Redis data structure and Performance OptimizationKnoldus Inc.

Redis Workshop on Data Structures, Commands, AdministrationHashedIn Technologies

An Introduction to Redis for .NET Developers.pdfStephen Lorello

Redis introductionFederico Daniel Colombo Gennarelli

Redis databaseÑáwrás Ñzár

Redis Functions, Data Structures for Web Scale AppsDave Nielsen

An Introduction to Redis for Developers.pdfStephen Lorello

Redis overviewAhmad El-khuja

Redis Modules - Redis India Tour - 2017HashedIn Technologies

Introduction to redisTanu Siwag

Redis — The AK-47 of Post-relational DatabasesKarel Minarik

Redis. Seattle Data Science and Data Engineering MeetupAbhishek Goswami

2_Redis.pptx very useful for begginers yessoufianbouktaib1

Introduction to RedisDvir Volk

Florida Man Uses Cache as Database.pdfStephen Lorello

10 Ways to Scale Your Website Silicon Valley Code Camp 2019Dave Nielsen

Redis/Lessons learnedTit Petric

redis-demo.pptxSNEHKUMAR30

Recently uploaded (20)

Software Developer Portfolio: Backend Architecture & Performance Optimizationkiwoong (daniel) kim

MODULE 4 BUILDING PLANNING AND DESIGN SY BTECH HVAC SYSTEM IN BUILDINGDr. BASWESHWAR JIRWANKAR

fy06_46f6-ht30_22_oil_gas_industry_guidelines.pptsukarnoamin

Kevin Corke Spouse Revealed A Deep Dive Into His Private Life.pdfMedicoz Clinic

Kevin Corke, a respected American journalist known for his work with Fox News, has always kept his personal life away from the spotlight. Despite his public presence, details about his spouse remain mostly private. Fans have long speculated about his marital status, but Corke chooses to maintain a clear boundary between his professional and personal life. While he occasionally shares glimpses of his family on social media, he has not publicly disclosed his wife’s identity. This deep dive into his private life reveals a man who values discretion, keeping his loved ones shielded from media attention.

ISO 4020-6.1- Filter Cleanliness Test Rig Catalogue.pdf FILTRATION ENGINEERING & CUNSULTANT

ISO 4020-6.1 – Filter Cleanliness Test Rig: Precision Testing for Fuel Filter Integrity Explore the design, functionality, and standards compliance of our advanced Filter Cleanliness Test Rig developed according to ISO 4020-6.1. This rig is engineered to evaluate fuel filter cleanliness levels with high accuracy and repeatability—critical for ensuring the performance and durability of fuel systems. 🔬 Inside This Presentation: Overview of ISO 4020-6.1 testing protocols Rig components and schematic layout Test methodology and data acquisition Applications in automotive and industrial filtration Key benefits: accuracy, reliability, compliance Perfect for R&D engineers, quality assurance teams, and lab technicians focused on filtration performance and standard compliance. 🛠️ Ensure Filter Cleanliness — Validate with Confidence.

ISO 4548-9 Oil Filter Anti Drain Catalogue.pdf FILTRATION ENGINEERING & CUNSULTANT

Fresh concrete Workability MeasurementSasiVarman5

world subdivision.pdf...................bmmederos10

Forensic Science – Digital Forensics – Digital Evidence – The Digital Forensi...ManiMaran230751

Software_Engineering_in_6_Hours_lyst1728638742594.pdfVanshMunjal7

Influence line diagram in a robust modelParthaSengupta26

May 2025: Top 10 Cited Articles in Software Engineering & Applications Intern...sebastianku31

The International Journal of Software Engineering & Applications (IJSEA) is a bi-monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Software Engineering & Applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern software engineering concepts & establishing new collaborations in these areas.

Digital Crime – Substantive Criminal Law – General Conditions – Offenses – In...ManiMaran230751

Application Security and Secure Software Development LifecycleDrKavithaP1

"The Enigmas of the Riemann Hypothesis" by Julio ChaiJulio Chai

In the vast tapestry of the history of mathematics, where the brightest minds have woven with threads of logical reasoning and flash-es of intuition, the Riemann Hypothesis emerges as a mystery that chal-lenges the limits of human understanding. To grasp its origin and signif-icance, it is necessary to return to the dawn of a discipline that, like an incomplete map, sought to decipher the hidden patterns in numbers. This journey, comparable to an exploration into the unknown, takes us to a time when mathematicians were just beginning to glimpse order in the apparent chaos of prime numbers. Centuries ago, when the ancient Greeks contemplated the stars and sought answers to the deepest questions in the sky, they also turned their attention to the mysteries of numbers. Pythagoras and his followers revered numbers as if they were divine entities, bearers of a universal harmony. Among them, prime numbers stood out as the cornerstones of an infinite cathedral—indivisible and enigmatic—hiding their ar-rangement beneath a veil of apparent randomness. Yet, their importance in building the edifice of number theory was already evident. The Middle Ages, a period in which the light of knowledge flick-ered in rhythm with the storms of history, did not significantly advance this quest. It was the Renaissance that restored lost splendor to mathe-matical thought. In this context, great thinkers like Pierre de Fermat and Leonhard Euler took up the torch, illuminating the path toward a deeper understanding of prime numbers. Fermat, with his sharp intuition and ability to find patterns where others saw disorder, and Euler, whose overflowing genius connected number theory with other branches of mathematics, were the architects of a new era of exploration. Like build-ers designing a bridge over an unknown abyss, their contributions laid the groundwork for later discoveries.

Introduction of Structural Audit and Health Montoring.pptxgunjalsachin

ISO 5011 Air Filter Catalogues .pdf FILTRATION ENGINEERING & CUNSULTANT

Proposed EPA Municipal Waste Combustor RuleAlvaroLinero2

Electrical and Electronics Engineering: An International Journal (ELELIJ)elelijjournal653

Highway Engineering - Pavement materialsAmrutaBhosale9

Software Developer Portfolio: Backend Architecture & Performance Optimizationkiwoong (daniel) kim

MODULE 4 BUILDING PLANNING AND DESIGN SY BTECH HVAC SYSTEM IN BUILDINGDr. BASWESHWAR JIRWANKAR

fy06_46f6-ht30_22_oil_gas_industry_guidelines.pptsukarnoamin

Kevin Corke Spouse Revealed A Deep Dive Into His Private Life.pdfMedicoz Clinic

ISO 4020-6.1- Filter Cleanliness Test Rig Catalogue.pdf FILTRATION ENGINEERING & CUNSULTANT

ISO 4548-9 Oil Filter Anti Drain Catalogue.pdf FILTRATION ENGINEERING & CUNSULTANT

Fresh concrete Workability MeasurementSasiVarman5

world subdivision.pdf...................bmmederos10

Forensic Science – Digital Forensics – Digital Evidence – The Digital Forensi...ManiMaran230751

Software_Engineering_in_6_Hours_lyst1728638742594.pdfVanshMunjal7

Influence line diagram in a robust modelParthaSengupta26

May 2025: Top 10 Cited Articles in Software Engineering & Applications Intern...sebastianku31

Digital Crime – Substantive Criminal Law – General Conditions – Offenses – In...ManiMaran230751

Application Security and Secure Software Development LifecycleDrKavithaP1

"The Enigmas of the Riemann Hypothesis" by Julio ChaiJulio Chai

Introduction of Structural Audit and Health Montoring.pptxgunjalsachin

ISO 5011 Air Filter Catalogues .pdf FILTRATION ENGINEERING & CUNSULTANT

Proposed EPA Municipal Waste Combustor RuleAlvaroLinero2

Electrical and Electronics Engineering: An International Journal (ELELIJ)elelijjournal653

Highway Engineering - Pavement materialsAmrutaBhosale9

Introduction to Redis Data Structures: Hashes

1. Hashes Introduction to Redis Data Structures:

2. What is Redis? Open Source, NoSQL Database Used by: Twitter, Pinterest, GitHub Stores Advanced Data Structures Client Support: Java, C, Node.js etc Introduction to Redis Data Structures: Hashes

3. What are Hashes? Hashes that map string names to string values They are essentially named containers of unique fields and their values A way to represent an object as a Redis data structure They also provide constant time basic operations like get, set, exists, etc. Introduction to Redis Data Structures: Hashes

4. Common Use Cases for Hashes Suited to store objects: sessions, users, visitors etc. Makes it one of key data structures provided by Redis In its memory optimized form, it is an excellent choice for caching large amounts of data Introduction to Redis Data Structures: Hashes

5. Hashes Operations in Redis HKEYS HLEN HGET HMGET HEXISTS HGETALL HDEL HINCRBY The complete list of set related Redis commands can be found here. Introduction to Redis Data Structures: Hashes

6. Internal Implementation Implemented as hash tables that use the hash function MurmurHash2 Grow via incremental resizing Hashes with few keys can be packed cleverly into linear array like structure Introduction to Redis Data Structures: Hashes

7. Redis Sets vs Redis Hashes Implemented as dictionaries Storage optimization made for smaller hashes Provide constant time basic operations Introduction to Redis Data Structures: Hashes Ziplist is used to optimize storage of smaller sorted sets and lists

8. Summary One of the well known recommendations for memory savings while using Redis is to use hashes instead of plain strings Small hashes are encoded in a very small space, so you should try representing your data using hashes every time it is possible Redis provides fairly useful and advanced operations on hashes Introduction to Redis Data Structures: Hashes

9. Sign Up for a free 30 day Trial Thanks for reading! Full Article here Hosting & management for MongoDB® and Redis®. NoSQL management Simplified. Click here for more information on Redis Hosting

Introduction to Redis Data Structures: Hashes

Recommended

More Related Content

What's hot (20)

Viewers also liked (10)

Similar to Introduction to Redis Data Structures: Hashes (20)

Recently uploaded (20)

Introduction to Redis Data Structures: Hashes