3/24/2023 0 Comments Ip camera cloud recording google![]() For example, when it comes to sorting, Spanner sorts NULLs and NaNs first whereas PostgreSQL sorts them last. Subtle SQL dialect differences can sometimes be hard to detect, especially when they are not explicitly defined in the SQL statements. Here are some additional areas that we considered to ensure a smooth migration. When planning for a migration, we often think about how to migrate the data, what changes to make on the application, which integrations to reconfigure, etc. In the future, should we implement new applications in a different language, we will be able to leverage the client library in that language. ![]() Spanner has client library support in 8 languages in addition to REST and RPC, covering most of the languages used within the team. The Nest engineering team is a distributed team with a distributed set of programming languages. Spanner is one of the only few services with 99.99% uptime for regional and 99.999% uptime for multi-regions with no planned maintenance windows. For example, running multiple databases via different services or in different regions means that we are constantly relying on a different service to accommodate variations in maintenance downtime. Maintenance windows, however planned, create significant additional work for the engineering team. Nest cameras need to have high uptimes, so our underlying services need to be constantly up as well. This capability allowed the engineering team to focus solely on the higher level application logic. Since Spanner is a relational database, it handles transactions processing for us in full. ![]() ![]() This challenge required significant special error handling, and the need to run reconciliation jobs. While performance was typically adequate, it lacked relational integrity, which placed a heavy burden on the application to handle cases where we needed to perform coordinated updates. Originally the Nest camera services were built on another cloud provider's distributed data store. During our benchmarks, there was little change to Spanner’s performance as we scaled our workload and instance size. In order for our customers to get updates, performance and consistency are key consideration factors. Nest cameras support uploading and watching recorded videos. This database works well for us because we can now scale as we grow without worrying about overgrowing the capacity constraints of our database. Spanner has unlimited scaling, and resizing is easy with just one click. We needed a system that could grow with our customers. Switching storage databases represents thousands of hours of engineering effort, and can increase the risk of downtime for our customers during the change. With a growing fleet of Nest cameras, we need to build for today and prepare for tomorrow. We wanted to ensure those requests would have consistent round trip time to provide predictability. Lower total cost of ownership - to create new experiences for our customers and focus on developing better camera software rather than maintaining a database, we required a fully managed service.īetter end user experience - users expect a consistent and predictable experience across their security devices and need to trust that these devices will perform as expected. This requirement meant choosing a database with no maintenance windows and minimal downtime. Minimal downtime - we know that cameras are used for security purposes, so minimizing downtime was key. Specifically, we were looking for the following customer benefits: Here are some of the factors we considered when choosing the next database for Nest cameras and why we selected Spanner. This article goes into details about what our requirements were, why we picked Cloud Spanner, what some of the challenges were during the migration, and some of the operational tasks we undertook after migrating to Spanner. To accomplish this, we needed to choose the right database. When we, the Nest team, set out to build the next generation platform to power millions of Nest cameras, it was essential to fulfill our video storage needs at large scale with reliability at the forefront.
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