---
title: "High-Throughput Graph Abstraction at Netflix: Part I"
url: https://daily.dev/posts/high-throughput-graph-abstraction-at-netflix-part-i-0dei7ppuk
source_url: https://netflixtechblog.com/high-throughput-graph-abstraction-at-netflix-part-i-e88063e6f6d5
type: article
source: "Netflix TechBlog"
published: 2026-05-29T19:12:26.685Z
updated: 2026-05-29T19:12:52.056Z
tags: ["architecture", "distributed-systems", "netflix"]
reading_time: 15
upvotes: 34
comments: 0
language: en
---

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# High-Throughput Graph Abstraction at Netflix: Part I

**[Netflix TechBlog](https://daily.dev/sources/netflix)** · 15 min read · 34 upvotes · 0 comments

## Summary

Netflix's Graph Abstraction is an OLTP-focused graph system handling up to 10 million operations per second across 650 TB of graph data with low latency. Built on top of existing Netflix data abstractions (Key-Value, TimeSeries, EVCache), it uses a Property Graph model with strongly typed schemas, forward/reverse edge indexes, and adjacency list storage. The architecture separates edge links from properties to prevent wide-row issues in Cassandra, employs write-aside and read-aside caching strategies, and enforces eventual consistency via Kafka-based entropy repair and async node deletions. A Gremlin-inspired gRPC traversal API supports multi-hop graph queries with filtering, sorting, and property selection pushdown. Single-hop traversals achieve single-digit millisecond p99 latency, while 2-hop traversals with high fanout stay under 50ms at p90.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://netflixtechblog.com/high-throughput-graph-abstraction-at-netflix-part-i-e88063e6f6d5>

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Tags: [#architecture](https://daily.dev/tags/architecture), [#distributed-systems](https://daily.dev/tags/distributed-systems), [#netflix](https://daily.dev/tags/netflix)

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