| Relevant to Cloud Firestore Enterprise edition only. |
This page describes the best practices when bulk loading data to Cloud Firestore with MongoDB compatibility
with tools like mongoimport.
Cloud Firestore is a highly distributed system offering automatic scaling to meet the needs of your business. Cloud Firestore dynamically splits and combines your data based on the load received by the system.
Load-based splitting happens automatically without any required pre-configuration. The Cloud Firestore load-based splitting system has some important, unique characteristics compared to other document databases that are important to keep in mind as you model your data.
Cloud Firestore's distributed nature can require changing some design choices to change, particularly for workloads that were optimized for databases where the primary replica is the bottleneck for write throughput.
Best Practices
Workloads that process large amounts of data in a single threaded client can create a bottleneck. Clients might be able to use single threading to bulk load data, as the throughput of the client and server are similarly matched. A Cloud Firestore database can handle significantly more parallelism, but this requires that you configure clients to send requests in parallel.
mongoimport
When using the mongoimport tool, requests are made sequentially by default.
To improve the load time into Cloud Firestore,
set the number of workers with the --numInsertionWorkers flag.
The correct setting might require tuning based on
the size of your client, but we generally recommend starting with at least 32.
async programming
When developing your own software using MongoDB compatible APIs, you can improve parallelism in the following ways:
- Async frameworks: using async frameworks let you process and respond to requests in parallel. It is not necessary to develop any complex pooling or queuing when making calls to your database. Each request flow can use independent connections and make their database calls in parallel.
- Use parallelized compute offerings: using services like Cloud Run, your system can scale the number of computation workers required to process data.
Transient Failures
When working with a large distributed system like Cloud Firestore, you might encounter transient failures such as network blips or contention on a document.
When bulk loading large amounts of information, it's important to maintain a retry strategy for failed writes without failing the larger bulk load operation.