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title: Build zero-downtime write architectures for Amazon Neptune
description: A detailed guide on building zero-downtime write architectures for Amazon Neptune using a write queue pattern. By placing a durable message queue (SQS, Kinesis...
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# Build zero-downtime write architectures for Amazon Neptune

**[AWS Database Blog](https://daily.dev/sources/aws-database-blog)** · 14 min read · 0 upvotes · 0 comments

## Summary

A detailed guide on building zero-downtime write architectures for Amazon Neptune using a write queue pattern. By placing a durable message queue (SQS, Kinesis Data Streams, or MSK) between the application and Neptune, writes are accepted continuously even during maintenance windows, failovers, and scaling operations. The post covers causes of Neptune write unavailability, how the queue pattern works, tradeoffs (eventual consistency, operational complexity, cost), a comparison table for choosing between SQS Standard/FIFO, KDS, and MSK, idempotency strategies using Gremlin mergeV()/mergeE() or openCypher MERGE, ordering options, monitoring with CloudWatch, read-after-write consistency strategies, and multi-Region deployment considerations with Neptune global databases.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://aws.amazon.com/blogs/database/build-zero-downtime-write-architectures-for-amazon-neptune>

## Questions this post answers

### How do I keep accepting writes to Amazon Neptune during a failover or maintenance window?

Place a durable message queue such as Amazon SQS, Kinesis Data Streams, or MSK between the application and Neptune, so writes are accepted into the queue and applied by a consumer once Neptune is available again. This decouples write acceptance from write execution, letting the application return success immediately even during failovers, patching, or scaling operations that can cause seconds to minutes of write unavailability.

_daily.dev surfaces architecture patterns like this for teams designing resilient graph database writes._

### Should I use SQS Standard, SQS FIFO, or Kinesis Data Streams as a write queue in front of Amazon Neptune?

Use SQS Standard as the default starting point when ordering doesn't matter, since it scales without a throughput ceiling. Choose SQS FIFO with a message group ID per entity when strict ordering within an entity is required, but note its 3,000-70,000 msg/sec range. Choose Kinesis Data Streams or MSK when replay capability or very high throughput with schema enforcement is needed.

_comparing queueing options for database writes is easier with real tradeoff breakdowns on daily.dev._

### How do I make Neptune writes idempotent when using an at-least-once delivery queue like SQS or Kinesis?

Use upsert semantics instead of creating new vertices: mergeV() and mergeE() in Gremlin, or MERGE in openCypher, so applying the same operation twice produces the same result. Combine this with SQS FIFO deduplication IDs for accidental duplicates and a DynamoDB idempotency table to record processed message IDs, avoiding redundant Neptune writes that waste CPU, memory, and I/O on the writer instance.

_developers wiring idempotent write pipelines for graph databases can track patterns like this on daily.dev._

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---

Tags: [#aws](https://daily.dev/tags/aws), [#architecture](https://daily.dev/tags/architecture)

[View this post on daily.dev](https://daily.dev/posts/build-zero-downtime-write-architectures-for-amazon-neptune-xzndru655)

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