Seattle, WA — Memurai, the company behind the Redis®* -compatible Memurai database, is excited to announce the launch of an enterprise edition of Valkey™, available for both Windows and Linux. Valkey, an open-source fork of Redis, is supported by major tech companies, including AWS, Google, Ericsson, Oracle, and Snap. 

Memurai’s enterprise, Valkey-compatible edition is designed for on-premises and edge computing environments where security, performance, and compliance are critical. Key features of this offering include: 
- **Windows and Linux Support:** Run Valkey natively on Linux and Windows without needing virtualization or emulation.  
-  **Comprehensive Maintenance:** Regular updates with hotfixes as needed.  
- **Expert Support:** Up to 24×7 support with a 1-hour response time.  
-  **Hardened Builds:** Built on a reproducible software supply chain using a hardened build toolchain.  
-  **Long-Term Support Options:** Ideal for applications that prioritize long-term stability over frequent updates and new features.  
- **Performance Enhancements:** Builds are optimized for performance, and the Windows edition has a custom, high-performance network stack.  
-  **Source Code Access:** While Memurai’s Valkey editions are proprietary, source code is still available for auditing and review.  
- **Upstream Contributions:** Where possible, Memurai contributes fixes and patches back to the Valkey community. 

 

If you’re looking for a secure, professionally supported, high-performance key/value data store, [fill out our form](https://www.memurai.com/valkey) to about our Valkey-compatible enterprise edition, or download the dev edition and test it yourself [here](https://www.memurai.com/get-memurai). 

 

*Redis® is a registered trademark of Redis Ltd and Valkey ™ is a registered trademark of the Linux Foundation. Any rights therein are reserved to Redis Ltd and the Linux Foundation. Any use by Memurai is for referential purposes only and does not indicate any sponsorship, endorsement, or affiliation between Redis, Linux Foundation, and Memurai.*

Find out about Memurai’s new enterprise edition of Valkey on Windows and Linux and get expert support for your Valkey deployments.

Memurai Launches Valkey™-Compatible Edition with Enterprise-Grade Support on Windows & Linux 

Memurai Launches Valkey Compatible Edition with Enterprise-Grade Support on Windows & Linux 

You might be here because, despite trawling through stack overflow forums, Jedis is still playing mind tricks with your Windows servers. If that's the case, then worry no more, the force of Memurai is here to help! Designed for Windows environments, Memurai is a high-performance in-memory cache and persistent datastore, fully compatible with Redis 7.2.  

In the first part of this blog, we’ll introduce you to the basics of setting up Jedis with Memurai, demonstrating its advantages over other solutions like Docker and WSL, and guiding you through creating a simple Java application for efficient data storage and retrieval. 

In the second part of this blog, we'll elevate the discussion to encompass advanced features suited for production-grade deployments. Part 2 is ideal for professionals seeking to scale their Java applications and enhance reliability and efficiency on Windows platforms. We'll delve into sophisticated capabilities like connection pooling, high availability with Redis Sentinels, and explore scaling your deployments with Redis clusters. Let’s dive in! 

  

  

## What is Jedis? 

Jedis - a clever portmanteau of Java and Redis - is a popular client library for interacting with Redis servers. It provides a Java API to connect, send commands, and retrieve data from a Redis server and allows developers to work with Redis in Java applications efficiently. 

  

## Comparing Memurai and Redis on Windows 

While Redis is a popular choice for many developers, it's not supported on Windows. This means that running Redis on a Windows server will require using Docker, WSL, or outdated versions that can compromise security and add complexity. Memurai, on the other hand, offers seamless integration with Windows, making it a more straightforward and efficient choice. For a deeper comparison, check out our blogs on Running Redis with Docker and WSL/WSL2. Memurai is 100% compatible with the latest Redis protocol, so client libraries that works with Redis will also work with Memurai, including Jedis. Let's show you how to set up Jedis on Windows without hassle. 

  

  

## Installation and Setup 

Before we dive into coding, it's essential to set up our environment. Memurai installation is straightforward and can be found in detail here. Once Memurai is up and running, let's focus on integrating Jedis, a popular Redis client library, with our Windows setup. 

### Setting Up Jedis: 

• Ensure you have Java installed on your system. 

• Include Jedis in your project's dependencies, typically via Maven or Gradle. 

  


## Part 1: Creating a Simple Java Application with Jedis and Memurai 

We will create a basic application that stores and retrieves data using Jedis and Memurai. This real-world example will help you understand how to implement key-value storage operations. 

### Step 1: Initialize a Jedis Connection 


```
import redis.clients.jedis.Jedis; 

public class JedisExample { 

    public static void main(String[] args) { 

        Jedis jedis = new Jedis("localhost", 6379); 

        System.out.println("Connected to Memurai!"); 

        // Further operations will go here. 

    } 

} 
```
  

This imports the necessary Jedis class and establishes a connection to a Memurai server running on the local machine (localhost) at the default Redis port (6379). When the connection is successful, it prints a message to the console indicating that the application is connected to Memurai. 

  

  

  

### Step 2: Basic Operations 


```
// Setting a value 

jedis.set("key", "value"); 

// Getting a value 

String value = jedis.get("key"); 

System.out.println("Retrieved value: " + value); 
```
  

  

This demonstrates how to perform basic key-value operations with Jedis. It shows how to use the set method to store a value associated with a key in Memurai and the get method to retrieve the value associated with a key. The retrieved value is then printed to the console. 

  

  

  

### Step 3: Error Handling and Best Practices 


```
try { 

    // Operations 

} catch (Exception e) { 

    e.printStackTrace(); 

} finally { 

    jedis.close(); 

} 
```
  

Remember to handle exceptions and ensure proper resource management by closing the Jedis connection in a final block. This helps to prevent resource leaks and ensures that the application behaves correctly in the event of an error. 

  

  

  

## Practical Applications and Use Cases for Jedis on Windows Servers 

The basic steps above serve as a starting point for developers looking to implement simple key-value storage operations using Jedis and Memurai/Redis on Windows. Combining Jedis and Memurai is ideal for a range of applications, from session caching in web applications to real-time analytics. The simplicity of our example can be expanded to suit more complex scenarios, such as managing shopping carts in e-commerce platforms or handling user sessions in large-scale applications.  

 

 

## Part 2: Advancing to Production-Grade Solutions with Memurai on Windows 

So far, we've laid the groundwork for using Jedis with Memurai to create robust Java applications. But what about scenarios where your application demands go beyond the basics? In the next sections, we'll explore advanced features that elevate your application to meet production-grade standards in Windows environments. 

In the upcoming sections, we'll explore sophisticated capabilities like connection pooling, Redis Sentinel support, and Redis cluster implementations. These topics are tailored for professionals aiming to elevate their Java applications in terms of scalability, reliability, and efficiency on Windows platforms. 

  

 

## Enhancing Performance with Connection Pools 

In production environments, managing Memurai/Redis on Windows connections efficiently is crucial. This is where connection pools come into play. They ensure that a pre-established number of connections are ready to be used when needed, reducing the latency and load associated with establishing a connection. 

 


```
import redis.clients.jedis.JedisPool; 

import redis.clients.jedis.JedisPoolConfig; 

  

public class RedisPool { 

    private static final JedisPool pool = new JedisPool(new JedisPoolConfig(), "localhost", 6379); 

  

    public static void main(String[] args) { 

        try (Jedis jedis = pool.getResource()) { 

            // Your code here 

        } 

    } 

} 
```
 
 
This code snippet sets up a JedisPool, which is a highly recommended practice for production-ready applications. 

 

## High Availability with Redis Sentinels on Windows 

For those requiring high availability, Redis Sentinels are essential. They monitor your Memurai/Redis instances and provide automatic failover support. Jedis offers built-in Sentinel support, ensuring your Java application can maintain a stable connection to Memurai/Redis even when the primary server fails. 

 


```
import redis.clients.jedis.JedisSentinelPool; 

  

public class RedisSentinel { 

    public static void main(String[] args) { 

        Set<String> sentinels = new HashSet<>(); 

        sentinels.add("localhost:26379"); // Sentinel address 

        try (JedisSentinelPool pool = new JedisSentinelPool("mymaster", sentinels)) { 

            // Your code here, using pool.getResource() 

        } 

    } 

} 
```
In this example, JedisSentinelPool is used to manage connections via Redis Sentinels. 

 

## Scaling Java Applications with Memurai and Redis Clusters on Windows 

Redis clusters offer a way to scale out your Memurai deployment for those with growing data and performance needs. Jedis has the capability to work with Redis clusters by distributing data across multiple Memurai/Redis nodes, which can be highly appealing for those considering complex, high-performance setups. 

 
```
import redis.clients.jedis.HostAndPort; 

import redis.clients.jedis.JedisCluster; 

  

public class RedisClusterExample { 

    public static void main(String[] args) { 

        Set<HostAndPort> nodes = new HashSet<>(); 

        nodes.add(new HostAndPort("localhost", 7000)); 

        try (JedisCluster jedisCluster = new JedisCluster(nodes)) { 

            // Your code here, using jedisCluster 

        } 

    } 

} 
```
 

This snippet demonstrates connecting to a Redis cluster using JedisCluster. 

  

## Conclusion 

By following the steps outlined in this blog, you'll have a solid foundation for using Jedis with Memurai, starting from basic setup to advanced production-grade solutions. While we've kept this discussion concise, for those eager to dive deeper, [Jedis documentation](https://github.com/redis/jedis#how-do-i-use-it.) and tutorials, especially [Baeldung's comprehensive guide](https://www.baeldung.com/jedis-java-redis-client-library), offer invaluable further insights and examples.  

  

This exploration into the world of Memurai/Redis on Windows and Jedis is just the beginning of what you can achieve in enhancing your Java applications and system efficiencies. Dive into Memurai's free developer edition, a perfect starting point for new users. For production environments that demand the utmost in efficiency and reliability, reach out to us [here](https://www.memurai.com/contact) to learn more about enhancing your data management capabilities with Memurai’s enterprise edition. 

  

Thank you for reading, and happy coding! 

Master Jedis on Windows with Memurai: a guide from basic setups to advanced usage for Java developers, minus Docker/WSL hassles. 

A Java Developer's Guide to Jedis on Windows with Memurai - the Windows-Native Port of Redis 

It's been years since most of the data generated by applications and different user interactions is no longer structured. We've shifted to capturing as much data as possible in various formats, including free text (like social media posts) because we now have the computation power to process this data properly.

And because of this, databases have started to migrate away from structured data as well. There are plenty of different types of NoSQL databases that deal with unstructured data, such as document-based databases, columnar databases, and even key-value databases.

Now, the key for any architect is to properly pick the right database based on their data needs. Choosing the right one will work towards improving your app's performance and making the life of your devs a lot easier.

So, let's take a look at what it means for you to move away from relational databases into a key-based one, such as Memurai.

# Understanding Key-Value Databases

Key-value databases, like Memurai, form a crucial part of the evolving landscape of data management. So, let's dive into what makes key-value databases unique and explore their operational principles.

## Key-Value Databases: Unveiling the Basics

In essence, a key-value database stores data as a collection of key-value pairs. Each piece of data is assigned a unique identifier, the "key," and is associated with a corresponding value. 
This basic structure simplifies the storage and retrieval process, enabling swift access to information without the constraints of rigid, tabular formats.
Operational simplicity is a hallmark of key-value databases. They leverage the principle of direct key-based access, allowing for rapid information retrieval. Unlike relational databases that rely on intricate joins and complex schemas, key-value databases excel in their ability to manage data in a straightforward manner.

That simplicity can be deceiving, though, considering that if you're coming from the relational world, you'll be used to having the ability to shift through data, perform searches, and mass update your records (to name a few examples). Key-based databases, on the other hand, are great when it comes to accessing single records (they're amazing at that), but reading and dealing with multiple records at the same time is not always an option.

That said, this doesn't mean they're limited; it just means developers need to change the way they think about data with key-based databases.

## Effectiveness in Handling Large-Scale, Unstructured Data

The shift towards capturing vast amounts of unstructured data, such as free text from social media posts, aligns seamlessly with the strengths of key-value databases. Their adaptability to handle diverse data formats, coupled with the speed of key-based access, makes them particularly effective in scenarios where the volume and variety of data are substantial.

Consider scenarios dealing with large-scale, dynamic datasets where the structure may evolve over time. Key-value databases shine in these environments, offering flexibility and scalability. Their ability to accommodate unstructured data without sacrificing performance positions them as a compelling choice for applications dealing with real-time, ever-expanding datasets.

In essence, the adoption of key-value databases reflects a strategic move towards a more streamlined and efficient data management paradigm. As architects navigate the landscape of database choices, understanding the fundamental principles and effectiveness of key-value databases becomes pivotal in making informed decisions that enhance both application performance and developer workflows.


# Challenges of Transitioning

Embarking on the journey from relational databases to key-value databases is a strategic move, but it comes with its set of challenges. 
Let's delve into the common hurdles encountered during this transition and explore effective strategies to overcome them.

## Data Modeling Dilemmas

One of the primary challenges lies in adapting to a different data modeling approach (that whole "changing the way your devs think about data" comment I made earlier). 

Relational data modeling thrives on structured, tabular data, often requiring intricate normalization to maintain data integrity. In some situations, even using very intricate entity relationships to model real-world scenarios. This approach makes dealing with relational databases very intuitive.

In contrast, key-value databases embrace a more flexible, schema-less design. There is no preset structure modeled, thus requiring a higher level of abstraction to "visualize" the use of the data. This is one of the major blockers when it comes to adoption, however, it's also what makes these databases so powerful and flexible.

To address this challenge, architects should embrace the schema flexibility offered by key-value databases. Instead of predefined structures, the focus shifts to designing data models that cater to specific application needs. This flexibility empowers developers to iterate swiftly, accommodating evolving data requirements seamlessly.

## Query Adjustments for Key-Based Access

Another significant hurdle emerges when transitioning from SQL queries, commonly used in relational databases, to key-based access in key-value databases. SQL queries often involve complex joins and aggregations, which may not align with the simplicity of key-value storage.
The key to overcoming this challenge is to rethink and rework query patterns. Instead of relying on intricate SQL queries to access their data, developers should leverage the direct access provided by keys and rethink their querying logic to work that way. 

This involves a shift in mindset towards designing queries that align with the decentralized nature of key-value databases. Additionally, caching mechanisms and indexing strategies can further optimize query performance.

### Best Practices for a Seamless Transition

To navigate these challenges effectively, consider implementing the following best practices:

- Thorough Assessment: Conduct a comprehensive analysis of the existing relational database structure and data access patterns before initiating the transition. This is key to identifying the problematic areas that will suffer the most from the transition.
- Incremental Migration: Break down the migration process into incremental steps, allowing for continuous testing and adjustment.
- Developer Training: Provide training sessions for developers to familiarize them with the new data modeling and query paradigms. Make sure every developer who should deal with the new data paradigm is properly trained before moving forward with a full migration.
- Testing Environments: Establish robust testing environments to validate the performance and functionality of the key-value database implementation.

In the end, the key to transitioning from relational to key-value databases is a proper strategic approach and a clear commitment to best practices (especially the ones listed above). 

# Benefits of Key-Value Databases

Let's now delve into the key benefits and examine real-world scenarios where these types of databases really shine.

## Scalability Beyond Boundaries

One of the standout features of key-value databases is their unparalleled scalability. Traditional relational databases often encounter bottlenecks when handling massive datasets or accommodating a growing user base. Key-value databases, on the other hand, excel in distributing data across clusters, allowing seamless horizontal scalability. This is mainly because of the lack of data schema, a simpler overall structure, and in the case of some of them, such as Redis (or their Windows counterpart, Memurai), the fact that they're in-memory databases makes them quite easy to scale.

Consider an e-commerce platform experiencing rapid growth in product listings and user interactions. Key-value databases enable effortless scaling to handle the increasing volume of transactions, ensuring a smooth and responsive user experience during peak shopping seasons.

## Flexibility in Data Modeling

Key-value databases embrace a schema-less design, providing a level of flexibility that is instrumental in adapting to evolving data requirements. Unlike rigid table structures in relational databases, key-value stores accommodate dynamic data models, allowing developers to iterate swiftly without the constraints of predefined schemas. This particular point greatly reduces development time, even after the application is in production. This is because, for structured databases, every single change in the schema requires a migration (along with the corresponding code changes). In the case of key-value databases, the code is the only aspect that needs to be changed.

Imagine a content management system where content types may vary widely; key-value databases empower content creators to define and modify data structures on the fly (imagine being able to create simple posts, image carousels, or perhaps even custom social media posts). This flexibility is definitely possible with a relational database, but only after crafting an intricate data structure to support it. Through the use of a key-value database such as Redis, this becomes trivial to implement.

## Performance Improvements Through Direct Key-Based Access

The efficiency of key-value databases stems from their direct key-based access, eliminating the need for complex join operations commonly associated with relational databases. This streamlined approach translates into faster data retrieval times and improved overall performance. This is one of the major reasons why architects go for these types of databases. Adding on top of this, the fact that the most common key-value databases (such as Memurai and Redis) store their information in memory (primarily), making data reads and writes incredibly faster than traditional databases that suffer from traditional I/O delays.

In scenarios demanding real-time analytics, key-value databases shine. By swiftly retrieving specific data points using keys, these databases excel in powering applications that require instantaneous insights. This is particularly evident in financial systems processing high-frequency trading data or social media platforms delivering personalized content in real-time.

## Harnessing the Power for Future Innovations

The benefits of key-value databases extend beyond immediate gains by future-proofing the applications they serve. 

As data continues to grow in volume and complexity, the scalability, flexibility, and performance advantages offered by key-value databases position them as key players in shaping the future of data management.

# Conclusion:

In summary, the migration from a structured database environment into one dominated by key-value databases can definitely be beneficial for the product and the development team as well.
By providing easy scalability, incredible data modeling flexibility, and unparalleled performance, key-value databases are the missing piece of any growing architecture.

Of course, like with any new piece of technology that you integrate into an existing project or team, the migration process needs to be properly structured, and best practices need to be followed before any big changes hit production.

If you'd like to understand how an in-memory key-value database can improve your application, give Memurai's [free developer edition](https://www.memurai.com/get-memurai) a try and check it out for yourself.


Explore the transition from traditional relational databases to key-value databases, their operational simplicity, effectiveness in handling unstructured data, and the challenges and strategies involved. 

Memurai Launches Valkey Compatible Edition with Enterprise-Grade Support on Windows & Linux

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