Data services

Data Services in the PDS platform encompass various applications that can be deployed, managed, and scaled according to application needs. These services form the backbone of application data storage and management, ensuring data integrity, availability, and performance.

For more information about the data services interface in PDS and the procedures to deploy the available data services,backup, restore, and manage data services, see Database deployments.

Data Services Overview

This section in the PDS UI provides a comprehensive, at-a-glance summary of all data services deployed within the platform. This section is crucial for administrators to monitor the status of each data service, enabling proactive management and troubleshooting.

Example: In an e-commerce organization, the Data Services Overview might show that the PostgreSQL database for order processing is healthy and performing well, while the MongoDB service for user sessions has a warning indicator due to high latency, prompting further investigation.

Data Services Configurations

This section allows users to configure specific settings for each data service. This customization is essential to tailor the data service performance and capabilities to the application requirements.

• Storage size: Set the amount of storage allocated to the data service, ensuring there is enough space for current and future data growth.
• Replication factors: Configure the number of replicas for the data service to ensure high availability and fault tolerance. More replicas mean higher data redundancy but also higher resource consumption.
• Performance tuning: Adjust settings such as cache size, connection limits, and query optimization parameters to enhance data service performance based on application workload.

Example: A finance organization might configure their PostgreSQL database with a high replication factor to ensure data is always available, even in the event of a node failure. They might also increase the storage size to accommodate a growing dataset and fine-tune performance settings for optimal query execution.

Resource Settings

This section allows administrators to manage the resources allocated to each data service, including CPU, memory, and storage. Proper resource allocation ensures that data services operate efficiently and meet performance expectations.

• CPU allocation: Assign specific CPU resources to data services, ensuring they have enough processing power for data operations.
• Memory allocation: Allocate adequate memory to data services to handle data caching and processing tasks, which can significantly impact performance.
• Storage allocation: Define the storage resources available to each data service, considering the type of storage (e.g., SSD or HDD) based on performance requirements.

Example: An online video streaming service might allocate more CPU and memory resources to their Cassandra database, which handles a high volume of read/write operations to manage user video playback and preferences efficiently.

Storage Options

This section enables users to choose from various storage types to meet their performance and cost requirements. Different applications have different storage needs, and PDS offers flexibility to select the appropriate storage type.

• SSDs (Solid State Drives): High-performance storage suitable for applications requiring fast read/write operations and low latency. Ideal for databases with high transaction volumes.
• HDDs (Hard Disk Drives): Cost-effective storage suitable for applications with less stringent performance requirements but high storage capacity needs. Ideal for archival and backup data.

Example: A data analytics organization might use SSDs for their real-time analytics database to ensure quick data processing and query response times, while using HDDs for long-term data storage and backup to reduce costs.

Data service deployment example

Consider a SaaS organization with the following data requirements:

PostgreSQL for application data

• Configuration: 1TB of SSD storage, high replication factor for fault tolerance, and optimized performance settings for fast query execution.
• Resource allocation: 8 CPUs and 32GB RAM to handle high transaction volumes and complex queries.
• Usage: Stores user data, transactions, and application metadata.

Redis for caching

• Configuration: 256GB of SSD storage, minimal replication due to its in-memory nature, and high-performance tuning for quick data retrieval.
• Resource allocation: 4 CPUs and 16GB RAM to manage large in-memory data sets efficiently.
• Usage: Caches frequently accessed data to reduce load on the primary database and speed up application response times.

Elasticsearch for logging and search:

• Configuration: 2TB of SSD storage, moderate replication for search availability, and performance settings optimized for indexing and search operations.
• Resource allocation: 16 CPUs and 64GB RAM to handle high indexing and search request loads.
• Usage: Stores and indexes log data for quick search and analytics, helping in monitoring and debugging application issues.

In this scenario, the SaaS organization leverages PDS to deploy and manage multiple data services tailored to their specific needs. By configuring storage, resource allocation, and performance settings appropriately, they ensure their applications run efficiently and reliably, providing a seamless experience for their users. This structured approach to data service management enhances overall operational efficiency and scalability.