Apache Cassandra 4.1 was a massive effort by the Cassandra community to build on what was released in 4.0, and it is the first of what we intend to be yearly releases. If you are using Cassandra and you want to know what’s new, or if you haven’t looked at Cassandra in a while and you wonder what the community is up to, then here’s what you need to know.
First off, let’s address why the Cassandra community is growing. Cassandra was built from the start to be a distributed database that could run across dispersed geographic locations, across different platforms, and to be continuously available despite whatever the world might throw at the service. If you asked ChatGPT to describe a database that today’s developer might need—and we did—the response would sound an awful lot like Cassandra.
Cassandra meets what developers need in availability, scalability, and reliability, which are things you just can’t bolt on afterward, however much you might try. The community has put a focused effort into producing tools that would define and validate the most stable and reliable database that they could, because it is what supports their businesses at scale. This effort supports everyone who wants to run Cassandra for their applications.
Guardrails for new Cassandra users
One of the new features in Cassandra 4.1 that should interest those new to the project is Guardrails, a new framework that makes it easier to set up and maintain a Cassandra cluster. Guardrails provide guidance on the best implementation settings for Cassandra. More importantly, Guardrails prevent anyone from selecting parameters or performing actions that would degrade performance or availability.
An example of this is secondary indexing. A good secondary index helps you improve performance, so having multiple secondary indexes should be even more beneficial, right? Wrong. Having too many can degrade performance. Similarly, you can design queries that might run across too many partitions and touch data across all of the nodes in a cluster, or use queries alongside replica-side filtering, which can lead to reading all the memory on all nodes in a cluster. For those experienced with Cassandra, these are known issues that you can avoid, but Guardrails make it easy for operators to prevent new users from making the same mistakes.
Guardrails are set up in the Cassandra YAML configuration files, based on settings including table warnings, secondary indexes per table, partition key selections, collection sizes, and more. You can set warning thresholds that can trigger alerts, and fail conditions that will prevent potentially harmful operations from happening.
Guardrails are intended to make managing Cassandra easier, and the community is already adding more options to this so that others can make use of them. Some of the newcomers to the community have already created their own Guardrails, and offered suggestions for others, which indicates how easy Guardrails are to work with.
To make things even easier to get right, the Cassandra project has spent time simplifying the configuration format with standardized names and units, while still supporting backwards compatibility. This provides an easier and more uniform way to add new parameters for Cassandra, while also reducing the risk of introducing any bugs.
Improving Cassandra performance
Alongside making things easier for those getting started, Cassandra 4.1 has also seen many improvements in performance and extensibility. The biggest change here is pluggability. Cassandra 4.1 now enables feature plug-ins for the database, allowing you to add capabilities and features without changing the core code.
In practice, this allows you to make decisions on areas like data storage without affecting other services like networking or node coordination. One of the first examples of this came at Instagram, where the team added support for RocksDB as a storage engine for more efficient storage. This worked really well as a one-off, but the team at Instagram had to support it themselves. The community decided that this idea of supporting a choice in storage engines should be built into Cassandra itself.
By supporting different storage or memtable options, Cassandra allows users to tune their database to the types of queries they want to run and how they want to implement their storage as part of Cassandra. This can also support more long-lived or persistent storage options. Another area of choice given to operators is how Cassandra 4.1 now supports pluggable schema. Previously, cluster schema was stored in system tables alone. In order to support more global coordination in deployments like Kubernetes, the community added external schema storage such as etcd.
Cassandra also now supports more options for network encryption and authentication. Cassandra 4.1 removes the need to have SSL certificates co-located on the same node, and instead you can use external key providers like HashiCorp Vault. This makes it easier to manage large deployments with lots of developers. Similarly, adding more options for authentication makes it easier to manage at scale.
There are some other new features, like new SSTable identifiers, which will make managing and backing up multiple SSTables easier, while Partition Denylists will make it easier to either allow operators full access to entire datasets or to reduce the availability of that data to set areas to ensure performance is not affected.
The future for Cassandra is full ACID
One of the things that has always counted against Cassandra in the past is that it did not fully support ACID (atomic, consistent, isolated, durable) transactions. The reason for this is that it was hard to get consistent transactions in a fully distributed environment and still maintain performance. From version 2.0, Cassandra used the Paxos protocol for managing consistency with lightweight transactions, which provided transactions for a single partition of data. What was needed was a new consensus protocol to align better with how Cassandra works.
Cassandra has filled this gap using Accord (PDF), a protocol that can complete consensus in one round trip rather than multiple transactions, and that can achieve this without leader failover mechanisms. Heading toward Cassandra 5.0, the aim is to deliver ACID-compliant transactions without sacrificing any of the capabilities that make Cassandra what it is today. To make this work in practice, Cassandra will support both lightweight transactions and Accord, and make more options available to users based on the modular approach that is in place for other features.
Cassandra was built to meet the needs of internet companies. Today, every company has similarly large-scale data volumes to deal with, the same challenges around distributing their applications for resilience and availability, and the same desire to keep growing their services quickly. At the same time, Cassandra must be easier to use and meet the needs of today’s developers. The community’s work for this update has helped to make that happen. We hope to see you at the upcoming Cassandra Summit where all of these topics will be discussed and more!
Patrick McFadin is vice president of developer relations at DataStax.
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New Tech Forum provides a venue to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to newtechforum@infoworld.com.
Ballerina, which is developed and supported by WSO2, is billed as “a statically typed, open-source, cloud-native programming language.” What is a cloud-native programming language? In the case of Ballerina, it is one that supports networking and common internet data structures and that includes interfaces to a large number of databases and internet services. Ballerina was designed to simplify the development of distributed microservices by making it easier to integrate APIs, and to do so in a way that will feel familiar to C, C++, C#, and Java programmers.
Essentially, Ballerina is a C-like compiled language that has features for JSON, XML, and tabular data with SQL-like language-integrated queries, concurrency with sequence diagrams and language-managed threads, live sequence diagrams synched to the source code, flexible types for use both inside programs and in service interfaces, explicit error handling and concurrency safety, and network primitives built into the language.
There are two implementations of Ballerina. The currently available version, jBallerina, has a toolchain implemented in Java, compiles to Java bytecode, runs on a Java virtual machine, and interoperates with Java programs. A newer, unreleased (and incomplete) version, nBallerina, cross-compiles to native binaries using LLVM and provides a C foreign function interface. jBallerina can currently generate GraalVM native images on an experimental basis from its CLI, and can also generate cloud artifacts for Docker and Kubernetes. Ballerina has interface modules for PostgreSQL, MySQL, Microsoft SQL Server, Redis, DynamoDB, Azure Cosmos DB, MongoDB, Snowflake, Oracle Database, and JDBC databases.
For development, Ballerina offers a Visual Studio Code plug-in for source and graphical editing and debugging; a command-line utility with several useful features; a web-based sandbox; and a REPL (read-evaluate-print loop) shell. Ballerina can work with OpenAPI, GraphQL schemas, and gRPC schemas. It has a module-sharing platform called Ballerina Central, and a large library of examples. The command-line utility provides a build system and a package manager, along with code generators and the interactive REPL shell.
Finally, Ballerina offers integration with Choreo, WSO2’s cloud-hosted API management and integration solution, for observability, CI/CD, and devops, for a small fee. Ballerina itself is free open source.
Ballerina Language
The Ballerina Language combines familiar elements from C-like languages with unique features. For an example using familiar elements, here’s a “Hello, World” program with variables:
import ballerina/io;
string greeting = "Hello";
public function main() {
string name = "Ballerina";
io:println(greeting, " ", name);
}
Both int and float types are signed 64-bit in Ballerina. Strings and identifiers are Unicode, so they can accommodate many languages. Strings are immutable. The language supports methods as well as functions, for example:
// You can have Unicode identifiers.
function พิมพ์ชื่อ(string ชื่อ) {
// Use u{H} to specify character using Unicode code point in hex.
io:println(ชื่u{E2D});
}
string s = "abc".substring(1, 2);
int n = s.length();
In Ballerina, nil is the name for what is normally called null. A question mark after the type makes it nullable, as in C#. An empty pair of parentheses means nil.
int? v = ();
Arrays in Ballerina use square brackets:
int[] v = [1, 2, 3];
Ballerina maps are associative key-value structures, similar to Python dictionaries:
map<int> m = {
"x": 1,
"y": 2
};
Ballerina records are similar to C structs:
record { int x; int y; } r = {
x: 1,
y: 2
};
You can define named types and records in Ballerina, similar to C typedefs:
type MapArray map<string>[];
MapArray arr = [
{"x": "foo"},
{"y": "bar"}
];
type Coord record {
int x;
int y;
};
You can create a union of multiple types using the | character:
type flexType string|int;
flexType a = 1;
flexType b = "Hello";
Ballerina doesn’t support exceptions, but it does support errors. The check keyword is a shorthand for returning if the type is error:
function intFromBytes(byte[] bytes) returns int|error {
string|error ret = string:fromBytes(bytes);
if ret is error {
return ret;
} else {
return int:fromString(ret);
}
}
This is the same function using check instead of if ret is error { return ret:
You can handle abnormal errors and make them fatal with the panic keyword. You can ignore return values and errors using the Python-like underscore _ character.
Ballerina has an any type, classes, and objects. Object creation uses the new keyword, like Java. Ballerina’s enum types are shortcuts for unions of string constants, unlike C. The match statement is like the switch case statement in C, only more flexible. Ballerina allows type inference to a var keyword. Functions in Ballerina are first-class types, so Ballerina can be used as a functional programming language. Ballerina supports asynchronous programming with the start, future, wait, and cancel keywords; these run in strands, which are logical threads.
This example on the Ballerina home page shows the code and sequence diagram for a program that pulls GitHub issues from a repository and adds each issue as a new row to a Google Sheet. The code and diagram are linked; a change to one will update the other. The access tokens need to be filled in at the question marks before the program can run, and the ballerinax/googleapis.sheets package needs to be pulled from Ballerina Central, either using the “Pull unresolved modules” code action in VS Code or using the bal pull command from the CLI.
Ballerina standard libraries and extensions
There are more than a thousand packages in the Ballerina Central repository. They include the Ballerina Standard Library (ballerina/*), Ballerina-written extensions (ballerinax/*), and a few third-party demos and extensions.
The standard library is documented here. The Ballerina-written extensions tend to be connectors to third-party products such as databases, observability systems, event streams, and common web APIs, for example GitHub, Slack, and Salesforce.
Anyone can create an organization and publish (push) a package to Ballerina Central. Note that all packages in this repository are public. You can of course commit your code to GitHub or another source code repository, and control access to that.
Installing Ballerina
You can install Ballerina by downloading the appropriate package for your Windows, Linux, or macOS system and then running the installer. There are additional installation options, including building it from the source code. Then run bal version from the command line to verify a successful installation.
In addition, you should install the Ballerina extension for Visual Studio Code. You can double-check that the extension installed correctly in VS Code by running View -> Command Palette -> Ballerina. You should see about 20 commands.
The bal command line
The bal command-line is a tool for managing Ballerina source code that helps you to manage Ballerina packages and modules, test, build, and run programs. It also enables you to easily install, update, and switch among Ballerina distributions. See the screen shot below, which shows part of the output from bal help, or refer to the documentation.
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bal help shows the various subcommands available from the Ballerina command line. The commands include compilation, packaging, scaffolding and code generation, and documentation generation.
Ballerina Examples
Ballerina has, well, a lot of examples. You can find them in the Ballerina by Example learning page, and also in VS Code by running the Ballerina: Show Examples command. Going through the examples is an alternate way to learn Ballerina programming; it’s a good supplement to the tutorials and documentation, and supports unstructured discovery as well as deliberate searches.
One caution about the examples: Not all of them are self-explanatory, as though an intern who knew the product wrote them without thinking about learners or having any review by naive users. On the other hand, many are self-explanatory and/or include links to the relevant documentation and source code.
For instance, in browsing the examples I discovered that Ballerina has a testing framework, Testarina, which is defined in the module ballerina/test. The test module defines the necessary annotations to construct a test suite, such as @test:Config {}, and the assertions you might expect if you’re familiar with JUnit, Rails unit tests, or any similar testing frameworks, for example the assertion test:assertEquals(). The test module also defines ways to specify setup and teardown functions, specify mock functions, and establish test dependencies.
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Ballerina Examples, as viewed from VS Code’s Ballerina: Show Examples command. Similar functionality is available online.
Overall, Ballerina is a useful and feature-rich programming language for its intended purpose, which is cloud-oriented programming, and it is free open source. It doesn’t produce the speediest runtime modules I’ve ever used, but that problem is being addressed, both by experimental GraalVM native images and the planned nBallerina project, which will compile to native code.
At this point, Ballerina might be worth adopting for internal projects that integrate internet services and don’t need to run fast or be beautiful. Certainly, the price is right.
Cost: Ballerina Platform and Ballerina Language: Free open source under the Apache License 2.0. Choreo hosting: $150 per component per month after five free components, plus infrastructure costs.
Platform: Windows, Linux, macOS; Visual Studio Code.
Dremio is adding new features to its data lakehouse including the ability to copy data into Apache Iceberg tables and roll back changes made to these tables.
Apache Iceberg is an open-source table format used by Dremio to store analytic data sets.
In order to copy data into Iceberg tables, enterprises and developers have to use the new “copy into SQL” command, the company said.
“With one command, customers can now copy data from CSV and JSON file formats stored in Amazon S3, Azure Data Lake Storage (ADLS), HDFS, and other supported data sources into Apache Iceberg tables using the columnar Parquet file format for performance,” Dremio said in an announcement Wednesday.
The copy operation is distributed across the entire, underlying lake house engine to load more data quickly, it added.
The company has also introduced a table rollback feature for enterprises, akin to a Windows system restore backup or a Mac Time Machine backup.
The tables can be backed up either to a specific time or a snapshot ID, the company said, adding that developers will have to make use of the “rollback” command to access the feature.
“The rollback feature makes it easy to revert a table back to a previous state with a single command. When rolling back a table, Dremio will create a new Apache Iceberg snapshot from the prior state and use it as the new current table state,” Dremio said.
Optimize command boosts Iceberg performance
In an effort to increase the performance of Iceberg tables, Dremio has introduced the “optimize” command to consolidate and optimize sizes of small files that are created when data manipulation commands such as insert, update, or delete are used.
“Often, customers will have many small files as a result of DML operations, which can impact read and write performance on that table and utilize excess storage,” the company said, adding that the “optimize” command can be used inside Dremio Sonar at regular intervals to maintain performance.
Dremio Sonar is a SQL engine that provides data warehousing capabilities to the company’s lakehouse.
The new features are expected to improve productivity of data engineers and system administrators while bringing utility to these class of users, said Doug Henschen, principal analyst at Constellation Research.
Dremio, which was an early proponent of Apache Iceberg tables in lakehouses, competes with the likes of Ahana and Starburst, both of which introduced support for Iceberg in 2021.
Other vendors such as Snowflake and Cloudera added support for Iceberg in 2022.
Dremio features new database, BI connectors
In addition to the new features, Dremio said that it was launching new connectors for Microsoft PowerBI, Snowflake and IBM Db2.
“Customers using Dremio and PowerBI can now use single sign-on (SSO) to access their Dremio Cloud and Dremio Software engines from PowerBI, simplifying access control and user management across their data architecture,” the company said.
The Snowflake and IBM DB2 connectors will allow enterprises to add Snowflake data warehouses and IBM DB2 databases as data sources for Dremio, it added.
This makes it easy to include data in these systems as part of the Dremio semantic layer, enabling customers to explore this data in their Dremio queries and views.
The launch of these connectors, according to Henschen, brings more plug-and-play options to analytics professionals from Dremio’s stable.
The rapid growth of data-intensive use cases such as simulations, streaming applications (like IoT and sensor feeds), and unstructured data has elevated the importance of performing fast database operations such as writing and reading data—especially when those applications begin to scale. Almost any component in a system can potentially become a bottleneck, from the storage and network layers through the CPU to the application GUI.
As we discussed in “Optimizing metadata performance for web-scale applications,” one of the main reasons for data bottlenecks is the way data operations are handled by the data engine, also called the storage engine—the deepest part of the software stack that sorts and indexes data. Data engines were originally created to store metadata, the critical “data about the data” that companies utilize for recommending movies to watch or products to buy. This metadata also tells us when the data was created, where exactly it’s stored, and much more.
Inefficiencies with metadata often surface in the form of random read patterns, slow query performance, inconsistent query behavior, I/O hangs, and write stalls. As these problems worsen, issues originating in this layer can begin to trickle up the stack and show to the end user, where they can show in form of slow reads, slow writes, write amplification, space amplification, inability to scale, and more.
New architectures remove bottlenecks
Next-generation data engines have emerged in response to the demands of low-latency, data-intensive workloads that require significant scalability and performance. They enable finer-grained performance tuning by adjusting three types of amplification, or writing and re-writing of data, that are performed by the engines: write amplification, read amplification, and space amplification. They also go further with additional tweaks to how the engine finds and stores data.
Speedb, our company, architected one such data engine as a drop-in replacement for the de facto industry standard, RocksDB. We open sourced Speedb to the developer community based on technology delivered in an enterprise edition for the past two years.
Many developers are familiar with RocksDB, a ubiquitous and appealing data engine that is optimized to exploit many CPUs for IO-bound workloads. Its use of an LSM (log-structured merge) tree-based data structure, as detailed in the previous article, is great for handling write-intensive use cases efficiently. However, LSM read performance can be poor if data is accessed in small, random chunks, and the issue is exacerbated as applications scale, particularly in applications with large volumes of small files, as with metadata.
Speedb optimizations
Speedb has developed three techniques to optimize data and metadata scalability—techniques that advance the state of the art from when RocksDB and other data engines were developed a decade ago.
Compaction
Like other LSM tree-based engines, RocksDB uses compaction to reclaim disk space, and to remove the old version of data from logs. Extra writes eat up data resources and slow down metadata processing, and to mitigate this, data engines perform the compaction. However, the two main compaction methods, leveled and universal, impact the ability of these engines to effectively handle data-intensive workloads.
A brief description of each method illustrates the challenge. Leveled compaction incurs very small disk space overhead (the default is about 11%). However, for large databases it comes with a huge I/O amplification penalty. Leveled compaction uses a “merge with” operation. Namely, each level is merged with the next level, which is usually much larger. As a result, each level adds a read and write amplification that is proportional to the ratio between the sizes of the two levels.
Universal compaction has a smaller write amplification, but eventually the database needs full compaction. This full compaction requires space equal or larger than the whole database size and may stall the processing of new updates. Hence universal compaction cannot be used in most real-time high performance applications.
Speedb’s architecture introduces hybrid compaction, which reduces write amplification for very large databases without blocking updates and with small overhead in additional space. The hybrid compaction method works like universal compaction on all the higher levels, where the size of the data is small relative to the size of the entire database, and works like leveled compaction only in the lowest level, where a significant portion of the updated data is kept.
Memtable testing (Figure 1 below) shows a 17% gain in overwrite and 13% gain in mixed read and write workloads (90% reads, 10% writes). Separate bloom filter tests results show a 130% improvement in read misses in a read random workload (Figure 2) and a 26% reduction in memory usage (Figure 3).
Tests run by Redis demonstrate increased performance when Speedb replaced RocksDB in the Redis on Flash implementation. Its testing with Speedb was also agnostic to the application’s read/write ratio, indicating that performance is predictable across multiple different applications, or in applications where the access pattern varies over time.
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Figure 1. Memtable testing with Speedb.
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Figure 2. Bloom filter testing using a read random workload with Speedb.
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Figure 3. Bloom filter testing showing reduction in memory usage with Speedb.
Memory management
The memory management of embedded libraries plays a crucial role in application performance. Current solutions are complex and have too many intertwined parameters, making it difficult for users to optimize them for their needs. The challenge increases as the environment or workload changes.
Speedb took a holistic approach when redesigning the memory management in order to simplify the use and enhance resource utilization.
A dirty data manager allows for an improved flush scheduler, one that takes a proactive approach and improves the overall memory efficiency and system utilization, without requiring any user intervention.
Working from the ground up, Speedb is making additional features self-tunable to achieve performance, scale, and ease of use for a variety of use cases.
Flow control
Speedb redesigns RocksDB’s flow control mechanism to eliminate spikes in user latency. Its new flow control mechanism changes the rate in a manner that is far more moderate and more accurately adjusted for the system’s state than the old mechanism. It slows down when necessary and speeds up when it can. By doing so, stalls are eliminated, and the write performance is stable.
When the root cause of data engine inefficiencies is buried deep in the system, finding it might be a challenge. At the same time, the deeper the root cause, the greater the impact on the system. As the old saying goes, a chain is only as strong as its weakest link.
Next-generation data engine architectures such as Speedb can boost metadata performance, reduce latency, accelerate search time, and optimize CPU consumption. As teams expand their hyperscale applications, new data engine technology will be a critical element to enabling modern-day architectures that are agile, scalable, and performant.
Hilik Yochai is chief science officer and co-founder of Speedb, the company behind the Speedb data engine, a drop-in replacement for RocksDB, and the Hive, Speedb’s open-source community where developers can interact, improve, and share knowledge and best practices on Speedb and RocksDB. Speedb’s technology helps developers evolve their hyperscale data operations with limitless scale and performance without compromising functionality, all while constantly striving to improve the usability and ease of use.
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New Tech Forum provides a venue to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to newtechforum@infoworld.com.
Database-as-a-service provider EnterpriseDB (EDB) has released the next generation of its popular distributed open source PostgreSQL database, dubbed EDB Postgres Distributed 5.0, designed to offer high availability, optimized performance and protection against data loss.
In contrast to its PostgreSQL14 offering, EDB’s Postgres Distributed 5.0 (PGD 5.0) offers a distributed architecture along with features such as logical replication.
In PGD 5.0 architecture, a node or database is a member of at least one node or database group and the most basic system would have a single node group for an entire cluster.
“Each node (database) participating in a PGD group both receives changes from other members and can be written to directly by the user,” the company said in a blog post.
“This is distinct from hot or warm standby, where only one master server accepts writes, and all the other nodes are standbys that replicate either from the master or from another standby,” the company added.
In order to enable high availability, enterprises can set up a PGD 5.0 system in such a way that each master node or database or server can be protected by one or more standby nodes, the company said.
“The group is the basic building block consisting of 2+ nodes (servers). In a group, each node is in a different availability zone, with dedicated router and backup, giving immediate switchover and high availability. Each group has a dedicated replication set defined on it. If the group loses a node, you can easily repair or replace it by copying an existing node from the group,” the company said.
This means that one node is the target for the main application and the other nodes are in shadow mode, meaning they are performing the read-write replica function.
This architectural setup allows faster performance as the main write function is occurring in one node, the company said, adding that “secondary applications might execute against the shadow nodes, although these are reduced or interrupted if the main application begins using that node.”
“In the future, one node will be elected as the main replicator to other groups, limiting CPU overhead of replication as the cluster grows and minimizing the bandwidth to other groups,” the company said.
Data protection is key
As enterprises generate an increasing amount of data, downtime of IT infrastructure can cause serious damage to enterprises. In addition, data center breaches are becoming more commonplace and a report from Uptime Institute’s 2022 Outage Analysis Report showed that 80% of data centers have experienced an outage in the past two years.
A separate report from IBM showed that data breaches have become very costly to deal with.
The distributed version of EDB’s object-relational database system, which competes with the likes of Azure’s Cosmos DB with Citius integration, is available as an add-on, dubbed EDB Extreme High Availability, for EDB Enterprise and Standard Plans, the company said.
In addition, EDB said that it will release the distributed version to all its managed database-as-a-service offerings including the Oracle-compatible BigAnimal and the AWS-compatible EDB Postgres Cloud Database Service.
The company expects to offer a 60-day, self-guided trial for PGD 5.0 soon. The distributed version supports PostgreSQL, EDB Postgres Extended Server and EDB Postgres Advanced Server along with other version combinations.
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