In-Depth with Apache Ignite

How can we process petabytes of data at the speed of RAM, without moving it across the network?

Today's Objectives

By the end of this lesson, you will be able to:

  • Define an In-Memory Data Grid and its architecture.
  • Compare and contrast Partitioned and Replicated cache modes.
  • Explain how compute collocation boosts performance.
  • Configure the REST API for basic cache operations.
  • Describe the process of horizontally scaling an Ignite cluster.

Lesson Roadmap

  1. Ignite Architecture: The In-Memory Data Grid
  2. Core Concept: Cache Modes
  3. Performance Secret: Compute Collocation
  4. Interacting with Ignite: The REST API
  5. Growing the Grid: Cluster Scaling
  6. Summary & Wrap-Up

Core Idea: What is Apache Ignite?

Ignite is a distributed In-Memory Data Grid (IMDG). It pools the RAM of multiple servers to create a high-performance data fabric. Unlike a simple cache, Ignite is a full computing platform designed to process data directly where it lives.

Apache Ignite Cluster Architecture Diagram Apache Ignite Cluster Server Node 1 Holds Data Partitions Executes Compute Jobs P1 P4 P7 Server Node 2 Holds Data Partitions Executes Compute Jobs P2 P5 P8 Server Node 3 Holds Data Partitions Executes Compute Jobs P3 P6 P9 Client Node (Thick Client) Topology Aware Does NOT hold data Application (e.g. REST, Thin Client) Connects to Cluster Interacts

Core Idea: Cache Modes

Ignite offers two main distributed cache modes. Partitioned mode splits data across nodes for massive scalability. Replicated mode copies the entire dataset to every node, providing extremely fast reads for smaller, frequently-accessed data.

Ignite Cache Modes: Partitioned vs. Replicated PARTITIONED MODE (Scalability) Full Dataset K1 K2 K3 K4 K5 Node 1 K1 K4 Node 2 K2 K5 Node 3 K3 Each node holds a unique subset of data. REPLICATED MODE (Fast Reads) Source Dataset K1 K2 K3 Node 1 Node 2 Node 3 Every node holds a full copy of the data.

Core Idea: Compute Collocation

Ignite's "secret sauce" is shipping computations to the data, not the other way around. By collocating related data on the same node using an affinity key, Ignite can run queries and jobs locally, eliminating costly network shuffling.

Ignite Compute Collocation vs. Traditional Data Fetch Traditional Approach: Move Data to Client Client DB Node ATrader Data DB Node BTrade Data 1. Fetch Trader 2. Fetch all Trades 3. JOIN happens here Expensive Network Traffic Ignite Collocation: Move Compute to Data Client Ignite Node A Ignite Node B Trader 'John' + All of John's Trades (Collocated) 1. Send Compute Job 2. JOIN happens here 3. Return small result set Minimal Network Traffic

Core Idea: REST API Interaction

For maximum compatibility, Ignite exposes a REST API over HTTP. It allows any language or tool to perform basic cache operations (get, put, remove) using simple URL commands, but it is not intended for high-performance workloads.

Apache Ignite REST API Flow REST API Interaction Flow External Application (Python, Shell Script, Web Browser) $ curl -X POST \"http://localhost:10800/ignite?cmd=put&cacheName=myCache&\key=user123&val=active_token" Ignite Node Client Connector Jetty Server on Port 10800 'myCache' [ user123: active_token ] 1. HTTP POST Request 2. Parse & Execute 3. JSON Response

Core Idea: Cluster Scaling & Discovery

Ignite scales horizontally by adding more server nodes. The Discovery SPI allows new nodes to find the cluster, typically via a static IP list. Once a node joins and the Baseline Topology is updated, data automatically rebalances across the grid.

Apache Ignite Horizontal Scaling Process Zero-Downtime Horizontal Scaling Process 1. Initial Cluster State Node 1 Node 1 Node 2 Node 2 Data is balanced across 2 nodes. 2. New Node Joins & Rebalances Node 1 Node 1 Node 2 Node 2 Node 3 Node 3 Discovery Data Rebalancing ... Node 3 discovers cluster, BLT is updated,and data redistribution begins. 3. Final Scaled State Node 1 Node 1 Node 2 Node 2 Node 3 Node 3 Data is evenly balanced across 3 nodes.

Check Your Understanding

  1. True or False: The `REPLICATED` cache mode is ideal for storing very large, terabyte-scale datasets. (False. Replicated mode is for small, read-heavy data; its size is limited by the smallest node's RAM.)
  2. True or False: By default, anyone on the network can access an Ignite node's REST API. (False. The REST API is disabled by default. If enabled, it should be bound to localhost or secured.)
  3. True or False: Adding a new node to the cluster using the static IP finder requires a full cluster restart. (False. Ignite is designed for zero-downtime scaling. New nodes can join a live cluster.)

Common Misconceptions

  • "Multicast discovery is fine for production."
    Correction: It's often disabled on corporate/cloud networks and is unreliable. Use the static `TcpDiscoveryVmIpFinder` for stability.
  • "Ignite is just a key-value cache."
    Correction: Treating it this way misses its main strengths. Leverage its distributed SQL and compute grid for maximum value.
  • "Scaling up (bigger servers) is better than scaling out."
    Correction: Scaling out (more servers) is Ignite's native model. It's more cost-effective, flexible, and avoids single points of failure.

Summary & Key Takeaways

  • Ignite is an In-Memory Data Grid, a powerful computing platform that pools RAM for extreme speed.
  • The choice between `PARTITIONED` (for scale) and `REPLICATED` (for read-speed) cache modes is fundamental.
  • Compute Collocation is a key performance feature that avoids network overhead by moving logic to data.
  • Scaling is done horizontally and with zero downtime by adding new nodes that discover the cluster and trigger data rebalancing.

Exit Ticket

Describe a specific application scenario where you would choose Apache Ignite over a simpler cache like Redis.

Justify your choice by referencing at least two specific Ignite features we discussed today.

Questions?