Click Below to Get the Code

Browse, clone, and build from real-world templates powered by Harper.
Solution
GitHub Logo

Distributed Apollo Cache

Deploy Apollo on Harper to create a distributed GraphQL caching service for easier and faster data fetching.
GraphQL
Solution
GraphQL

Distributed Apollo Cache

Apollo
at Harper
April 30, 2025
Apollo
at Harper
April 30, 2025
Apollo
at Harper
April 30, 2025
April 30, 2025
Deploy Apollo on Harper to create a distributed GraphQL caching service for easier and faster data fetching.
Apollo

Deliver a Better Experience for Developers and Users

Are you tired of sluggish apps and convoluted data fetching? Apollo and Harper offer a powerful solution. This dynamic duo combines the industry-leading GraphQL server, Apollo, with a robust distributed systems platform. Together, they create a seamless GraphQL data-fetching and caching service designed for blazing-fast performance and unmatched developer accessibility. Read on to see how Apollo and Harper unlock a superior experience for both the developer and the end user.

Shedding Light on Apollo’s Multi-Server Problem

Despite GraphQL's promise of efficient data fetching, traditional implementations like Apollo can introduce performance bottlenecks. Cascading cross-network requests and the overhead of serializing and deserializing data at each step contribute to significant latency and cost. Even in a simple scenario of retrieving data from a single API behind Apollo, there might be as many as 10 serialization steps! At scale, this client-Apollo-API-data system loop translates directly to increased latency, high operational costs, and, ultimately, a sluggish user experience.

Introducing Distributed GraphQL Queries with Cache 

Harper’s elegant combination of distributed application and data functionalities allow GraphQL queries to be resolved without sending requests to additional servers unless absolutely necessary. This eliminates several network hops and can decrease the number of serialization and deserialization steps to two (compared to ten for a typical Apollo deployment). Additionally, multiple nodes can be distributed for multi-region near-user data access, removing the need for frequent high-latency requests to central systems. With both passive caching and active storage options, tuning your GraphQL data service layer to balance latency and cost is easy. By deploying Apollo on Harper, query requests and data lookup functions are seamlessly unified, which reduces compute requirements system-wide while lowering latency and costs.

6 Benefits of Deploying Apollo on Harper

Submillisecond Lookups

Deploying distributed GraphQL servers in the same process as an in-memory cache delivers unbeatable response times. Even in high throughput scenarios, most experience submillisecond p95 response times when cached values are available, unlocking lightning-fast experiences.  

Passive & Active Caching

For maximum cache hit rate, proactively populate your cache with a change data capture layer to ensure the best user experience for every user, every time. Alternatively, utilize a standard passive caching approach, ensuring fast performance after an initial request populates the cache. 

One Call, Cache Everywhere

Unlike CDN solutions that build their cache in isolation, Harper’s native cross-node data synchronization can replicate values to all globally connected nodes in milliseconds, giving your origin a break from repeated lookups for the same data.  

Flexibility Beyond Apollo

To deliver more advanced services, leverage Harper’s native application engine and streaming functions to quickly achieve outcomes beyond what Apollo can provide. 

Horizontal Scale

Ensure seamless client experiences with a GraphQL caching server that scales horizontally to meet demand. Harper's ability to scale horizontally while distributing data across regions eliminates bottlenecks while guaranteeing low latency for users everywhere.

Deploy in Weeks

With components already built, deploying in a single sprint is easy. Simply define the GraphQL schema and resolvers within Harper and deploy your containerized service near all user population centers.

The Best Way to Deploy Apollo

Many technology teams migrate to GraphQL for data fetching efficiency and development simplicity, but the user experience often remains stagnant. Apollo on Harper changes this by dramatically reducing total server load and network latency, which accelerates user experience. Don't just take our word for it—see the difference firsthand. Contact Harper for a complimentary proof of concept.

Deliver a Better Experience for Developers and Users

Are you tired of sluggish apps and convoluted data fetching? Apollo and Harper offer a powerful solution. This dynamic duo combines the industry-leading GraphQL server, Apollo, with a robust distributed systems platform. Together, they create a seamless GraphQL data-fetching and caching service designed for blazing-fast performance and unmatched developer accessibility. Read on to see how Apollo and Harper unlock a superior experience for both the developer and the end user.

Shedding Light on Apollo’s Multi-Server Problem

Despite GraphQL's promise of efficient data fetching, traditional implementations like Apollo can introduce performance bottlenecks. Cascading cross-network requests and the overhead of serializing and deserializing data at each step contribute to significant latency and cost. Even in a simple scenario of retrieving data from a single API behind Apollo, there might be as many as 10 serialization steps! At scale, this client-Apollo-API-data system loop translates directly to increased latency, high operational costs, and, ultimately, a sluggish user experience.

Introducing Distributed GraphQL Queries with Cache 

Harper’s elegant combination of distributed application and data functionalities allow GraphQL queries to be resolved without sending requests to additional servers unless absolutely necessary. This eliminates several network hops and can decrease the number of serialization and deserialization steps to two (compared to ten for a typical Apollo deployment). Additionally, multiple nodes can be distributed for multi-region near-user data access, removing the need for frequent high-latency requests to central systems. With both passive caching and active storage options, tuning your GraphQL data service layer to balance latency and cost is easy. By deploying Apollo on Harper, query requests and data lookup functions are seamlessly unified, which reduces compute requirements system-wide while lowering latency and costs.

6 Benefits of Deploying Apollo on Harper

Submillisecond Lookups

Deploying distributed GraphQL servers in the same process as an in-memory cache delivers unbeatable response times. Even in high throughput scenarios, most experience submillisecond p95 response times when cached values are available, unlocking lightning-fast experiences.  

Passive & Active Caching

For maximum cache hit rate, proactively populate your cache with a change data capture layer to ensure the best user experience for every user, every time. Alternatively, utilize a standard passive caching approach, ensuring fast performance after an initial request populates the cache. 

One Call, Cache Everywhere

Unlike CDN solutions that build their cache in isolation, Harper’s native cross-node data synchronization can replicate values to all globally connected nodes in milliseconds, giving your origin a break from repeated lookups for the same data.  

Flexibility Beyond Apollo

To deliver more advanced services, leverage Harper’s native application engine and streaming functions to quickly achieve outcomes beyond what Apollo can provide. 

Horizontal Scale

Ensure seamless client experiences with a GraphQL caching server that scales horizontally to meet demand. Harper's ability to scale horizontally while distributing data across regions eliminates bottlenecks while guaranteeing low latency for users everywhere.

Deploy in Weeks

With components already built, deploying in a single sprint is easy. Simply define the GraphQL schema and resolvers within Harper and deploy your containerized service near all user population centers.

The Best Way to Deploy Apollo

Many technology teams migrate to GraphQL for data fetching efficiency and development simplicity, but the user experience often remains stagnant. Apollo on Harper changes this by dramatically reducing total server load and network latency, which accelerates user experience. Don't just take our word for it—see the difference firsthand. Contact Harper for a complimentary proof of concept.

Deploy Apollo on Harper to create a distributed GraphQL caching service for easier and faster data fetching.

Download

White arrow pointing right
Deploy Apollo on Harper to create a distributed GraphQL caching service for easier and faster data fetching.

Download

White arrow pointing right
Deploy Apollo on Harper to create a distributed GraphQL caching service for easier and faster data fetching.

Download

White arrow pointing right

Explore Recent Resources

News
GitHub Logo

Harper Recognized as an Honorable Mention in the 2026 Gartner® Magic Quadrant™ for Cloud-Native Application Platforms

Harper’s inclusion highlights a broader shift toward integrated cloud-native platforms that unify data, caching, messaging, and application logic. The article explains how Harper’s distributed runtime and managed infrastructure reduce operational complexity, improve performance, and support modern applications and AI agents that require fast, reliable access to state, events, and tools.
Announcement
News
Harper’s inclusion highlights a broader shift toward integrated cloud-native platforms that unify data, caching, messaging, and application logic. The article explains how Harper’s distributed runtime and managed infrastructure reduce operational complexity, improve performance, and support modern applications and AI agents that require fast, reliable access to state, events, and tools.
Person with short dark hair and moustache, wearing a colorful plaid shirt, smiling outdoors in a forested mountain landscape.
Aleks Haugom
Senior Manager of GTM
News

Harper Recognized as an Honorable Mention in the 2026 Gartner® Magic Quadrant™ for Cloud-Native Application Platforms

Harper’s inclusion highlights a broader shift toward integrated cloud-native platforms that unify data, caching, messaging, and application logic. The article explains how Harper’s distributed runtime and managed infrastructure reduce operational complexity, improve performance, and support modern applications and AI agents that require fast, reliable access to state, events, and tools.
Aleks Haugom
Aug 2026
News

Harper Recognized as an Honorable Mention in the 2026 Gartner® Magic Quadrant™ for Cloud-Native Application Platforms

Harper’s inclusion highlights a broader shift toward integrated cloud-native platforms that unify data, caching, messaging, and application logic. The article explains how Harper’s distributed runtime and managed infrastructure reduce operational complexity, improve performance, and support modern applications and AI agents that require fast, reliable access to state, events, and tools.
Aleks Haugom
News

Harper Recognized as an Honorable Mention in the 2026 Gartner® Magic Quadrant™ for Cloud-Native Application Platforms

Harper’s inclusion highlights a broader shift toward integrated cloud-native platforms that unify data, caching, messaging, and application logic. The article explains how Harper’s distributed runtime and managed infrastructure reduce operational complexity, improve performance, and support modern applications and AI agents that require fast, reliable access to state, events, and tools.
Aleks Haugom
News
GitHub Logo

Harper 5.2: More Throughput per Node, Fewer Systems Around It

Harper 5.2 helps architecture and platform leaders improve performance, control infrastructure costs, secure production workloads, and reduce operational complexity by bringing faster data access, agentic capabilities, scheduling, backup, routing, and protection into one unified runtime.
Product Update
News
Harper 5.2 helps architecture and platform leaders improve performance, control infrastructure costs, secure production workloads, and reduce operational complexity by bringing faster data access, agentic capabilities, scheduling, backup, routing, and protection into one unified runtime.
Person with short dark hair and moustache, wearing a colorful plaid shirt, smiling outdoors in a forested mountain landscape.
Aleks Haugom
Senior Manager of GTM
News

Harper 5.2: More Throughput per Node, Fewer Systems Around It

Harper 5.2 helps architecture and platform leaders improve performance, control infrastructure costs, secure production workloads, and reduce operational complexity by bringing faster data access, agentic capabilities, scheduling, backup, routing, and protection into one unified runtime.
Aleks Haugom
Aug 2026
News

Harper 5.2: More Throughput per Node, Fewer Systems Around It

Harper 5.2 helps architecture and platform leaders improve performance, control infrastructure costs, secure production workloads, and reduce operational complexity by bringing faster data access, agentic capabilities, scheduling, backup, routing, and protection into one unified runtime.
Aleks Haugom
News

Harper 5.2: More Throughput per Node, Fewer Systems Around It

Harper 5.2 helps architecture and platform leaders improve performance, control infrastructure costs, secure production workloads, and reduce operational complexity by bringing faster data access, agentic capabilities, scheduling, backup, routing, and protection into one unified runtime.
Aleks Haugom
Blog
GitHub Logo

5 Architectures for Web Personalization

Personalization is a data-delivery problem. Every architectural choice reduces to two distances: compute to user, and compute to fresh data. This piece maps five real architectures against both axes, scored on a concrete retailer workload where stale or slow data breaks the business.
Blog
Personalization is a data-delivery problem. Every architectural choice reduces to two distances: compute to user, and compute to fresh data. This piece maps five real architectures against both axes, scored on a concrete retailer workload where stale or slow data breaks the business.
Person with short dark hair and moustache, wearing a colorful plaid shirt, smiling outdoors in a forested mountain landscape.
Aleks Haugom
Senior Manager of GTM
Blog

5 Architectures for Web Personalization

Personalization is a data-delivery problem. Every architectural choice reduces to two distances: compute to user, and compute to fresh data. This piece maps five real architectures against both axes, scored on a concrete retailer workload where stale or slow data breaks the business.
Aleks Haugom
Jul 2026
Blog

5 Architectures for Web Personalization

Personalization is a data-delivery problem. Every architectural choice reduces to two distances: compute to user, and compute to fresh data. This piece maps five real architectures against both axes, scored on a concrete retailer workload where stale or slow data breaks the business.
Aleks Haugom
Blog

5 Architectures for Web Personalization

Personalization is a data-delivery problem. Every architectural choice reduces to two distances: compute to user, and compute to fresh data. This piece maps five real architectures against both axes, scored on a concrete retailer workload where stale or slow data breaks the business.
Aleks Haugom