Release Date: August-2026

This page describes product changes, along with new features and functionality for GigaSpaces release 17.3.0.

Live Space Operations & Traffic Tracker

Administrators can now inspect real-time and historical database activity on any active Space Where GigaSpaces data is stored. It is the logical cache that holds data objects in memory and might also hold them in layered in tiering. Data is hosted from multiple SoRs, consolidated as a unified data model. instance using a lightweight, server-side tracking mechanism.

• Live Inspection: Adds dedicated tracking endpoints (/operations/active and /operations/last) to see exactly what queries are running right now or have just finished executing.

• SpaceDeck GigaSpaces intuitive, streamlined user interface to set up, manage and control their environment. Using SpaceDeck, users can define the tools to bring legacy System of Record (SoR) databases into the in-memory data grid that is the core of the GigaSpaces system. Visibility: Introduces a brand-new Operations & Traffic tab in SpaceDeck, showcasing rich diagnostic details such as the exact query string, client IP, affected partition ID, duration, and error codes.

• On-Demand Toggling: Controlled entirely via an LRMI monitoring toggle REST API REpresentational State Transfer. Application Programming Interface An API, or application programming interface, is a set of rules that define how applications or devices can connect to and communicate with each other. A REST API is an API that conforms to the design principles of the REST, or representational state transfer architectural style. so it can be enabled exclusively during troubleshooting periods to preserve overhead.

For more information, see Monitoring and Network Monitoring.

Tiered Storage "Push Down" Optimizations

To significantly boost performance, specific operations can bypass the GigaSpaces execution engine entirely and execute directly on the underlying storage tier (SQLite).

• Optimized Clear Table Operations: Bypasses one-by-one object processing in favor of a direct "Drop and recreate table" approach when no query conditions exist.

• Safe Fallbacks: Includes strict validation checks (e.g., checking for unindexed conditions, active transactions, and HA configurations) to safely fall back to safer processing channels when push-down conditions aren't met.

Hot-Pluggable Metric Providers Framework

This release standardizes how metrics are queried from external telemetry systems by providing a unified query API wrapper (GET /api/v3/metrics).

• Hot-Pluggable Backends: Third-party metric platforms (such as InfluxDB, Datadog, or Dynatrace) can be plugged in or swapped on the fly by implementing the GsMetricsQueryProvider Java interface.

• Dynamic Loading: New providers can be registered live using the JAR upload API or by dropping them into the optional metrics directory—no system restarts required.

• Note on Prometheus: Out-of-the-box special relations for Prometheus have been removed from standard configurations; please use the generic OTLP provider instead.

For API structures, time-range querying parameters, and developer implementation details, see Configuration.

Partition-Level Distributed Tracing Split

To simplify performance debugging across highly distributed configurations, operations that span multiple partitions are now split natively within your tracing ecosystem.

• Granular Child Spans: Overarching cluster operations (like read_multiple) break down into individual child spans for every affected partition.

• Isolation of Slow Nodes: Each partition-level trace accurately details its unique processing duration, status (UNSET or error states), and execution properties (db.gigaspaces.partition_id).

To configure your OpenTelemetry tracing systems to catch partition-level child spans, see Distributed Tracing.

Space Comparison Engine

A brand-new comparison capability has been introduced to validate consistency and spot schema or data divergence between distinct spaces. This capability is highly integrated into disaster recovery and multi-region topology synchronization checks.

For details on using the space comparison tool within distributed environments, see Multi-Site Replication.

Customized Data Integration & Pluggable Connectors

This release introduces the ability to implement custom integration flows while maintaining centralized management, tracking, and observability across pipelines via the DI The Data Integration (DI) layer is a vital part of the Digital Integration Hub (DIH) platform. It is responsible for a wide range of data integration tasks such as ingesting data in batches or streaming data changes. This is performed in real-time from various sources and systems of record (SOR. The data then resides in the In-Memory Data Grid (IMDG), or Space, of the GigaSpaces Smart DIH platform.-Manager and SpaceDeck. Developers can now build custom connector services that expose standard pipeline lifecycle REST REpresentational State Transfer. Application Programming Interface An API, or application programming interface, is a set of rules that define how applications or devices can connect to and communicate with each other. A REST API is an API that conforms to the design principles of the REST, or representational state transfer architectural style. APIs (start, stop, status, and info).

• Pluggable Architecture: Custom connectors act as standalone data sources. They are registered with a dedicated URL scheme (pluggable://...) without requiring any initial service disruption.

• Decoupled Control: The DI-Manager automatically handles status polling and maps custom connector responses directly to UI health states (RUNNING, FAILING, STOPPED).

• Pluggable Kafka Apache Kafka is a distributed event store and stream-processing platform. Apache Kafka is a distributed publish-subscribe messaging system. A message is any kind of information that is sent from a producer (application that sends the messages) to a consumer (application that receives the messages). Producers write their messages or data to Kafka topics. These topics are divided into partitions that function like logs. Each message is written to a partition and has a unique offset, or identifier. Consumers can specify a particular offset point where they can begin to read messages. Connectors: A flexible, YAML-based data integration solution that seamlessly connects Apache Kafka to GigaSpaces, enabling real-time ingestion of JSON, AVRO, XML, or CSV data from Kafka topics into GigaSpaces spaces, with support for complex transformations, CDC Change Data Capture. A technology that identifies and captures changes made to data in a database, enabling real-time data integration and synchronization between systems. Primarily used for data that is frequently updated, such as user transactions. operations (Insert/Update/Delete), and automatic batching. It is implemented as a customized data integration; see Kafka-GigaSpaces Connector Features for more information.

For details on implementing a custom connector service, see Customised Data Integration.

MCP Server (Preview Mode)

In version 17.2.2 we introduce the MCP Server, bringing native Model Context Protocol (MCP) support to GigaSpaces clusters. Currently in Preview Mode, this feature allows customers to expose XAP GigaSpaces eXtreme Application Platform. Provides a powerful solution for data processing, launching, and running digital services's ultra-low-latency data tier and distributed compute capabilities directly to autonomous AI agents and agentic workflows.

To protect grid environments from aggressive or runaway AI agent behavior, every deployed MCP service includes configurable Rate Limiting (operations per minute) and utilizes Service Account tokens to enforce strict role-based access control.

The Agentic Compute & Execution Engine

Instead of moving massive enterprise datasets across the network to an LLM—which wastes critical time and API tokens—you can now bring the AI's code instructions directly to the data.

AI agents can leverage a suite of dedicated User-Defined Logic & Compute Tools to trigger high-performance processing natively inside the cluster.

Modular MCP Configurations & Flexible System Capabilities

Administrators can instantiate multiple, independent MCP servers targeted at different business needs. Through the user interface, you can precisely configure each service by selecting only the specific functional toolsets required for that target, allowing you to define grid administrator agent, various logic running agents and agents who change data based on insights from other flows. See more at XAP MCP Tools Overview

Semantic Layer

The Semantic Layer serves as the foundational bridge between Large Language Models (LLMs) and the XAP data model, automatically translating complex cluster schemas into clear, business-friendly concepts that AI agents can effortlessly navigate.

The entire Semantic Layer is hosted on a specialized, dedicated space configured with a META mode. It includes Unified Cluster Metadata & Relationships and User-Defined Tool & Parameter Descriptions. To further empower agentic decision-making, the Semantic Layer will be enhanced to actively track and expose real-time statistics on each table in future versions.

See more at Semantic Layer Foundation

Vector Search

Vector Search is an XAP extension that enables semantic similarity search on high-dimensional vector embeddings. Instead of exact keyword matching, you can find conceptually similar items based on their vector representations. See more at Vector Search.

We value your input! As these capabilities are in preview, we want to hear about your unique use cases. Please contact us to collaborate on optimizing your agentic workflows or if your system requires any additional capabilities.

Redolog drop packet listener

Added ability to listen to drop packets in the embedded space see: Dropped Packet Listener