Selecting the right pattern

when managing complex data structures, choosing the right pattern can significantly impact the efficiency and effectiveness of your app embedded maps are slow documents that exceed 5 mb do not sync with other connected peers if a document exceeds 250 kb in size, stdout warning prints to the console you can embed a map in another map to create an embedded map comprised of multiple, hierarchical levels for example, consider a people collection that contains documents with the following schema such a schema translates to a document with a nested map cars where each car can be arbitrarily large { "name" "susan", "age" 43, "cars" { "abc123" { "make" "hyundai", "color" "red", "mileage" 13000 }, "def456" { "make" "jeep", "color" "blue", "mileage" 34000 } } deeply nested objects result in slow replication and data store performance as follows in scenarios with a limited internet connection or exclusive use of bluetooth low energy (le) for syncing across connected peers, replicating documents that contain large amounts of data experiences a slowdown for instance, relying solely on bluetooth le for a document of typical size results in a maximum replication rate of 20 kb per second consequently, a document of 250 kb or more may take 10 seconds or more to replicate for the first time between devices there could still be slow replication and store performance on faster transports such as lan or p2p wifi this is because the end‑to‑end replication process requires breaking down documents into smaller parts before syncing them across the mesh only once the client receives all the smaller parts and reconstructs them, is the document returned therefore, the callback is unable to render, store, and return a large document in a timely manner flat models are faster instead of using a single document to encode all of your large datasets, use a series of smaller documents nesting multiple map objects in a single document may result in slow sync performance so, rather than using a single document to encode a large dataset, use a series of smaller documents, which are inexpensive in terms of storage and processing resources ditto considers collections inexpensive in terms of storage and processing resources ditto uses a combination of the collection name and the document id to sync and query documents, and collections serve as an index for a set of related documents you can use this index to establish foreign key relationships between them by referencing the document id for more information, see docid 4aywtez9sut17pmqw lh4 since the document id is immutable, only use the flat model in situations where you have static identifiers within your foreign key relationship for example, consider a people collection and a cars collection in the following example, each car has an owner — represented by the id ownerid field — which relates to the person's document id in the people collection { " id" "abc123", "name" "susan", "age" 31 } in the cars collection { " id" { "id" "def456", "ownerid" "abc123" }, "make" "hyundai", "color" "red", } evaluation criteria the decision to use deeply embedded objects in a single document or opt for a flat model instead depends on your specific requirements, relationships between data, and tolerance for certain tradeoffs when assessing whether to embed a nested structure in a document or distribute the data across multiple smaller documents, refer to the following criteria to guide you docid\ sh9dlm9mi8gwzgxld2nmd docid\ sh9dlm9mi8gwzgxld2nmd docid\ sh9dlm9mi8gwzgxld2nmd docid\ sh9dlm9mi8gwzgxld2nmd query performance the following table provides criteria for the speed and efficiency of read and write operations criteria model you often retrieve or update pairs of embedded items together embedded map you frequently access or modify only certain parts of the data flat data size syncing large documents can significantly impact network performance caution is advised when handling large binary data, such as a high resolution image or video exceeding 250kb, a deeply embedded document, or a large document instead of storing large files, such as an avatar image exceeding 250 kilobytes, directly within a document object, consider using the attachment data type for more information, see docid\ egicr0evnaijbudbqskay criteria model it is unlikely for the embedded map to potentially impact overall system performance embedded you expect that the embedded map will become more complex and larger over time, making it difficult to manage and potentially causing system performance to degrade flat concurrency the following table provides criteria for the potential for concurrent edits and resulting merge conflicts criteria model your end users will likely modify the same data items stored locally on their respective devices while internet is unavailable embedded it is unlikely that your end users will simultaneously modify the same data items stored locally on their respective devices when the internet is unavailable flat document structure the following table provides a criteria for assessing the overall structure of your documents criteria model your embedded map are relatively simple in structure and do not require maintainability over time embedded multiple embedded map structures are becoming deeply nested; as in there are embedded structures representing three or more levels in a hierarchy resulting in a necessity for better organization flat