How Netflix Scales its API with GraphQL Federation (Part 2)
In our previous post and QConPlus talk, we discussed GraphQL Federation as a solution for distributing our GraphQL schema and implementation. In this post, we shift our attention to what is needed to run a federated GraphQL platform successfully — from our journey implementing it to lessons learned. Our Journey so FarOver the past year, we’ve implemented the core infrastructure pieces necessary fo
How Netflix microservices tackle dataset pub-sub
By Ammar Khaku IntroductionIn a microservice architecture such as Netflix’s, propagating datasets from a single source to multiple downstream destinations can be challenging. These datasets can represent anything from service configuration to the results of a batch job, are often needed in-memory to optimize access and must be updated as they change over time. One example displaying the need for d
Metacat: Making Big Data Discoverable and Meaningful at Netflix
by Ajoy Majumdar, Zhen Li Most large companies have numerous data sources with different data formats and large data volumes. These data stores are accessed and analyzed by many people throughout the enterprise. At Netflix, our data warehouse consists of a large number of data sets stored in Amazon S3 (via Hive), Druid, Elasticsearch, Redshift, Snowflake and MySql. Our platform supports Spark, Pre
1
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
処理を実行中です
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
