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  • Overview
  • Quick introduction to Apache Geode
  • Implementation

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  1. Components
  2. Processors
  3. Enrichment
  4. Dynamic contextual data

Caching architecture

Caching is a key enabler for low-latency contextual data access

PreviousDynamic contextual dataNextStatic contextual data

Last updated 6 months ago

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Overview

The Joule architecture integrates a client cache into the process, allowing real-time data access from a distributed managed caching service.

This solution is generally only needed whenever a processes need to be triggered intra-day whenever key data changes at source. This is a common pattern with larger enterprise solution architectures and is also becoming more applicable to scale-ups.

Quick introduction to Apache Geode

Apache Geode is a recognised enterprise grade caching solution battle tested across multiple industries often in highly complex environments.

Over 20+ years the product has been deployed within healthcare, financial services, telco and many other industries and thus has many architecture deployment patterns.

The Joule implementation provides the eventing callbacks to bind the backing database to the distributed cache cluster using Geode's cache listener APIs. Any changes with the cluster are reflected with the embedded Joule client cache. And thus the client cache always has an updated image of the data stored locally.

Implementation

geode stores:
    name: mobile reference data
    connection:
        locator address: 192.168.86.39
        locator port: 41111
    stores:
      mobiledevices:
        region: mobile-devices
        keyClass : java.lang.String
        gii: true
  1. Bind to the store within the enricher processor by using its logical mapping name. The enricher using assignment binds to the underlying store.

enricher:
  fields:      
    deviceManufacturer:
      by key: tac
      with values: [deviceManufacturer, year_released]
      using: deviceStore 
      
  stores:
    deviceStore:
      store name: mobiledevices

The reference architecture solution is built using although solutions such as and are good candidates too and would be considered in the future.

Its key value proposition is providing a low latency and high throughput data to client services using a multi-region distributed cluster solution, along with vertical scaling techniques. Geode implement proving the user to decide upon which two CAP guarantees to follow.

Set up the Apache Geode cluster, see Geode .

Configure client cache connection, see for more information on setup.

Apache Geode
Redis
Hazelcast
CAP theorem
documentation
Contextual data
Apache Geode reference solution