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Thanos is a project that turns your Prometheus installation into a highly available metric system with unlimited storage capacity. From a very high-level view, it does this by deploying a sidecar to Prometheus, which uploads the data blocks to any object storage. A store component downloads the blocks again and makes them accessible to a query component, which has the same API as Prometheus itself. This works nicely with Grafana because its the same API.

In the previous blog posts in this series, we introduced the Netflix Media DataBase (NMDB) and its salient “Media Document” data model. In this post we will provide details of the NMDB system architecture beginning with the system requirements—these will serve as the necessary motivation for the architectural choices we made. A fundamental requirement for any lasting data system is that it should scale along with the growth of the business applications it wishes to serve.

Uber, like most large technology companies, relies extensively on metrics to effectively monitor its entire stack. From low-level system metrics, such as memory utilization of a host, to high-level business metrics, including the number of Uber Eats orders in a particular city, they allow our engineers to gain insight into how our services are operating on a daily basis. As our dimensionality and usage of metrics increases, common solutions like Prometheus and Graphite become difficult to manage and sometimes cease to work.

The number of shards on each node, and tries to balance the number of shards per node evenly across the clusterThe high and low disk watermarks. Elasticsearch considers the available disk space on a node before deciding whether to allocate new shards to that node or to actively relocate shards away from that node. A nodes that has reached the low watermark (i.e 80% disk used) is not allowed receive any more shards.

Dropbox stores petabytes of metadata to support user-facing features and to power our production infrastructure. The primary system we use to store this metadata is named Edgestore and is described in a previous blog post, (Re)Introducing Edgestore. In simple terms, Edgestore is a service and abstraction over thousands of MySQL nodes that provides users with strongly consistent, transactional reads and writes at low latency. Edgestore hides details of physical sharding from the application layer to allow developers to scale out their metadata storage needs without thinking about complexities of data placement and distribution.