Some years ago, when storing data, we mainly used RDBMS (Relational DataBase Management System) such as MySQL and PostgreSQL. But recently, we started using Firestore more. Although the role of storing data is the same, the usage and design concept of Firestore is very different from RDBMS.
In this article, we will check the limitations of Firestore, and what are the differences from RDB.
No aggregate function
In RDB, it is relatively easy to perform COUNT(*) or SUM(amount) for hundreds or thousands of records.
However, this is not practical with Firestore.
For example, suppose we have a screen that displays a list of 20 slips, each of which has 10 details attached to it.
If we want to display the "total amount" in the list, we can get the result quickly in RDB by doing SUM in a subquery.
On the other hand, if we want to do the same thing with Firestore, we need to load 200 documents on the client-side and aggregate them as a "process". This causes a problem in terms of cost and performance.
In such a case, we update the "total amount" of the slip data when adding or updating the details data, and refer only to the slip data when displaying it on the screen.
For other constraints as well, it is necessary to think in terms of creating and updating data with reading situations in mind at the time of writing data.
Not good at searching
To search with two or more conditions, we need to create an index.
In addition, some queries cannot be executed even if any indexes are created.
In a compound query, range (<, <=, >, >=) and not equals (!=, not-in) comparisons must all filter on the same field.
For example, we cannot do a range search on two columns, such as "Created after A, modified after B". Also, we cannot do a range search and sort with two different columns.
We need a workaround, such as changing the screen specification or using a different search column to perform an exact match search.
For example, in the above example, if we can change the specification to "search data that was created in a specific month and updated after B", we add a new column that is the year-month of created (the string "yyyyMM"). Therefore we can change one of the conditions to an exact match.
No full text search
If we want to do a full-text search, such as searching for posts that contain a specific string, it is common to use an external service such as Algolia.
Cloud Firestore doesn't support native indexing or search for text fields in documents.
Additionally, downloading an entire collection to search for fields client-side isn't practical.
We can also use Firebase Extensions called "Search with Algolia" to seamlessly sync our Firestore data to Algolia.
In RDBMS, paging can perform by specifying "offset".
In Firestore, "limit" can be specified, but "offset" cannot be specified.
Therefore, it is difficult to support specifications such as loading by page number.
On the other hand, loading continuous data such as infinite scrolling can be easy.
Cannot update or delete multiple data by conditions
It is not possible to update all the flags of the data that match the condition or delete all the related data when the parent data was deleted.
It is necessary to retrieve the data by conditions and then update or delete them individually.
There are functions such as transactions and batches, it is possible to update and delete data atomically.
However, only 500 writes can be done in one transaction.
Maximum number of writes that can be passed to a Commit operation or performed in a transaction 500
When using RDBs, an API is usually implemented between the client and the DB.
Therefore, it is common to implement read restrictions as "processes" on the API side.
In the case of Firestore, read restrictions are defined as a security rule, it is common for clients to refer directly to Firestore.
By setting the security rule, various restrictions can be applied to each collection.
However, it is not possible to set restrictions such as "only certain fields can be viewed" within a single data collection.
Therefore, if there are two types of user information, one for public and the other for private, it is recommended to manage them in separate collections.
Cannot write frequently
Maximum sustained write rate to a document 1 per second
Sustaining a write rate above once per second increases latency and causes contention errors. This is not a hard limit, and you can surpass the limit in short bursts.
As mentioned earlier, aggregation by queries is not possible, so to display the number of "likes" on a post, it needs to be counted up as an integer.
However, "likes" may be done for a single post from many users, it is likely to be limited to the soft limit above.
An answer is available on the official page.
To support more frequent counter updates, create a distributed counter.
Each counter is a document with a subcollection of "shards," and the value of the counter is the sum of the value of the shards.
Simply put, by storing the data in 10 counter documents, the soft limit is reduced to about 1/10. And by summing them when loading, the loading cost is reduced.
Firestore has a lot of advantages, such as relatively low cost of operation and easy implementation of real-time change detection.
However, the direction it aims to take and the situations in which it excels are different from those of the RDBMSs.
I think it is important to understand the limitations and use them in the right place.
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