33 docs tagged with "Community"

The ScalarDB library is available on the Maven Central Repository. You can add the library as a build dependency to your application by using Gradle or Maven.

This document explains how to configure the underlying databases of ScalarDB to make applications that use ScalarDB work correctly and efficiently.

This document explains how to create your custom values file for the ScalarDB Analytics with PostgreSQL chart. For details on the parameters, see the README of the ScalarDB Analytics with PostgreSQL chart.

This tutorial describes how to create a sample application that supports microservice transactions in ScalarDB.

This tutorial describes how to create a sample application that supports the multi-storage transactions feature in ScalarDB.

This document explains how to get started with log aggregation for Scalar products on Kubernetes using Grafana Loki (with Promtail).

This document explains how to get started with Scalar products monitoring on Kubernetes using Prometheus Operator (kube-prometheus-stack). Here, we assume that you already have a Mac or Linux environment for testing. We use Minikube in this document, but the steps we will show should work in any Kubernetes cluster.

This guide explains how to get started with ScalarDB Analytics with PostgreSQL by using a Helm Chart in a Kubernetes cluster as a test environment. In addition, the contents of this guide assume that you already have a Mac or Linux environment set up for testing. Although minikube is mentioned, the steps described should work in any Kubernetes cluster.

This getting started tutorial explains how to configure your preferred database in ScalarDB and illustrates the process of creating a sample e-commerce application, where items can be ordered and paid for with a credit card by using ScalarDB. The sample e-commerce application shows how users can order and pay for items by using a line of credit.

This document explains how to get started with ScalarDB Analytics with PostgreSQL. We assume that you have already installed ScalarDB Analytics with PostgreSQL and that all required services are running. If you don't have such an environment, please follow the instructions in How to Install ScalarDB Analytics with PostgreSQL in Your Local Environment by Using Docker. Because ScalarDB Analytics with PostgreSQL executes queries via PostgreSQL, we also assume that you already have a psql client or another PostgreSQL client to send queries to PostgreSQL.

This getting started tutorial explains how to configure your preferred database in ScalarDB and set up a basic electronic money application by using Kotlin. Since Kotlin has Java interoperability, you can use ScalarDB directly from Kotlin.

Since ScalarDB provides transaction capabilities on top of non-transactional or transactional databases non-invasively, you need to take special care to back up and restore the databases in a transactionally consistent way.

This document explains how to deploy ScalarDB Analytics with PostgreSQL by using Scalar Helm Charts. For details on the custom values file for ScalarDB Analytics with PostgreSQL, see Configure a custom values file for ScalarDB Analytics with PostgreSQL.

This document explains how to set up a local environment that runs ScalarDB Analytics with PostgreSQL using the multi-storage back-end of Cassandra, PostgreSQL, and DynamoDB local server using Docker Compose.

This guide explains how to scale ScalarDB. The contents of this guide assume that you used Scalar Helm Chart to deploy ScalarDB Cluster, which is the recommended way.

This guide explains how to upgrade to a newer version of ScalarDB.

Data modeling (or in other words, designing your database schemas) is the process of conceptualizing and visualizing how data will be stored and used by identifying the patterns used to access data and the types of queries to be performed within business operations.

ScalarDB transactions can span multiple storages or databases while maintaining ACID compliance by using a feature called multi-storage transactions.

This page describes the required tools and their versions to use ScalarDB correctly.

This tutorial describes how to run analytical queries on sample data by using ScalarDB Analytics with PostgreSQL.

ScalarDB is a cross-database HTAP engine. It achieves ACID transactions and real-time analytics across diverse databases to simplify the complexity of managing multiple databases.

This page includes a list of release notes for ScalarDB 3.9.

This tutorial describes how to run benchmarking tools for ScalarDB. Database benchmarking is helpful for evaluating how databases perform against a set of standards.

This page describes the available configurations for ScalarDB.

For details about the design and implementation of ScalarDB, please see the following documents, which we presented at the VLDB 2023 conference:

ScalarDB FDW is a PostgreSQL extension that implements a foreign data wrapper (FDW) for ScalarDB.

The ScalarDB Java API is mainly composed of the Administrative API and Transactional API. This guide briefly explains what kinds of APIs exist, how to use them, and related topics like how to handle exceptions.

This page describes what ScalarDB is and its primary use cases.

The following are sample applications for ScalarDB:

ScalarDB has its own data model and schema that maps to the implementation-specific data model and schema. In addition, ScalarDB stores internal metadata, such as transaction IDs, record versions, and transaction statuses, to manage transaction logs and statuses when you use the Consensus Commit transaction manager.

Schema Importer is a CLI tool for automatically configuring PostgreSQL. By using this tool, your PostgreSQL database can have identical database objects, such as namespaces and tables, as your ScalarDB instance.

This page explains how to use the Storage API for users who are experts in ScalarDB.

ScalarDB supports executing transactions with a two-phase commit interface. With the two-phase commit interface, you can execute a transaction that spans multiple processes or applications, like in a microservice architecture.