merged stream with two different tables
#merged stream with two different tables
hollow relic
dire hollyBOT
Hi @hollow relic. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:
You can create a merged stream from two different tables in Convex using the streaming helpers, but you need to be aware of how the data is structured and ordered. The mergedStream function is designed to merge multiple streams—typically from the same table or with compatible index fields—by interleaving their results according to a specified order.
If you want to merge streams from two different tables, you can do so as long as the streams are ordered by compatible fields. For example, if both tables have a _creationTime field, you can merge them by that field. Here’s a conceptual example:
import { stream, mergedStream } from "convex-helpers/server/stream";
import schema from "./schema";
// Stream from table1
const stream1 = stream(ctx.db, schema)
.query("table1")
.order("desc"); // or use .withIndex(...) if needed
// Stream from table2
const stream2 = stream(ctx.db, schema)
.query("table2")
.order("desc");
// Merge the streams by _creationTime
const merged = mergedStream([stream1, stream2], ["_creationTime"]);
// Paginate or collect results
const results = await merged.paginate(args.paginationOpts);
Important notes:
- The merged streams must be ordered by the same fields (e.g.,
["_creationTime"]). - The resulting stream will interleave documents from both tables according to the specified order.
- The documents from each table may have different shapes, so you may want to normalize or map them before merging, depending on your use case.
This pattern is supported and described in the Convex documentation and articles, which show that you can merge streams from different sources as long as the ordering is compatible and you handle the resulting data shape appropriately Merging Streams of Convex data.
If you need to join data from two tables (rather than just merge), consider using flatMap and map to perform join-like operations, as shown in the Streamed Joins (flatMap) section.
If you have a more specific use case or need to handle pagination or filtering, let me know!
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
hollow relic
@dire holly what's the performance of such streams?
dire hollyBOT
Let me check the knowledge base for you again. One moment! :books:
The performance of merged streams in Convex depends primarily on how the underlying streams are constructed and the indexes used. When you use mergedStream, Convex efficiently interleaves results from each stream based on the specified order (e.g., by _creationTime). Each stream must already be ordered by the merge fields, and mergedStream yields the next document from the stream that comes next in the order. This is generally efficient, as it avoids loading all data into memory and instead streams results incrementally as needed.
However, there are some important considerations:
- If you use filtering with TypeScript predicates (e.g., filterWith), you may end up reading more data than necessary, which can slow down your query or even cause it to crash if too much data is read. To mitigate this, you can set a maximumRowsRead option in your pagination options to limit how much data is scanned before returning a page of results.
- The efficiency of mergedStream is similar to how SQL engines perform unions, but with the advantage that Convex streams use the indexes you specify, ensuring efficient access patterns. In contrast, SQL engines may sometimes fall back to full table scans.
- If you merge streams from different tables, as long as both are ordered by compatible fields (like _creationTime), the merging itself is efficient, but you should be mindful of the shape and size of the data being merged.
In summary, merged streams are designed to be efficient as long as you leverage indexes and avoid excessive in-memory filtering. For very large datasets or complex filters, always consider the impact on performance and use pagination and row limits as needed to keep queries responsive and cost-effective Merging Streams of Convex data Filtering.
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
hollow relic
@dire holly show me how to iterate over merged stream
dire hollyBOT
Let me check the knowledge base for you again. One moment! :books:
To iterate over a merged stream in Convex, you use the mergedStream helper to combine multiple streams, and then you can consume the merged stream using methods like .take(n), .collect(), or .paginate(opts). If you want to manually iterate over the merged stream (for example, to process each item as it arrives), you can use a for await...of loop, since streams are async iterables.
Here’s an example of how to merge two streams and iterate over the results:
import { stream, mergedStream } from "convex-helpers/server/stream";
import schema from "./schema";
// Create two streams, e.g., from the same or different tables
const stream1 = stream(ctx.db, schema)
.query("messages")
.withIndex("from_to", q => q.eq("from", u1).eq("to", u2));
const stream2 = stream(ctx.db, schema)
.query("messages")
.withIndex("from_to", q => q.eq("from", u2).eq("to", u1));
// Merge the streams by _creationTime
const merged = mergedStream([stream1, stream2], ["_creationTime"]);
// Iterate over the merged stream
for await (const message of merged) {
// Process each message here
console.log(message);
}
You can also use .take(n) to get the first n items, or .collect() to get all items as an array, or .paginate(opts) for paginated results. The for await...of pattern is useful for processing items as they are streamed, which is efficient for large datasets or when you want to process items incrementally.
This approach is supported and described in the Convex documentation on merging streams Merging Streams of Convex data.
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
hollow relic
@dire holly show me with two different tables
dire hollyBOT
Let me check the knowledge base for you again. One moment! :books:
I'm sorry, but the knowledge sources do not contain an explicit example of iterating over a merged stream from two different tables in Convex. The documentation and examples provided focus on merging streams from the same table or from streams with compatible index fields, but do not show a concrete code sample for merging and iterating over streams from two different tables.
The general pattern for merging streams (using mergedStream) is shown for streams from the same table, and while it is suggested that you can merge streams as long as they are ordered by compatible fields (such as _creationTime), there is no direct example or confirmation for two different tables. If you need to merge streams from two different tables, you would need to ensure both streams are ordered by the same field, but the documentation does not provide a code sample or further guidance for this scenario Merge the streams.
If you have a more specific use case or further details, please provide them, and I can help clarify based on the available documentation.
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
hollow relic
@dire holly how to change response of each stream
dire hollyBOT
Let me check the knowledge base for you again. One moment! :books:
To change (transform) the response of each stream in Convex, you can use the .map method on a stream. The .map method allows you to modify each document in the stream before it is returned, such as adding, removing, or changing fields. This works for both individual streams and streams created via joins or flatMap.
Here’s an example of using .map to change the response of a stream:
const pageOfMessageBodies = stream(ctx.db, schema)
.query("messages")
.map((message) => message.body)
.paginate(args.paginationOpts);
You can also use .map after a join (e.g., after flatMap) to combine or transform fields from both tables:
const messagesFromFriends = friends
.flatMap((friend) =>
stream(ctx.db, schema)
.query("messages")
.withIndex("from_to", q => q.eq("from", friend.friendId).eq("to", args.user))
.map((message) => ({
fromBestFriend: friend.isBest,
friendshipStarted: friend._creationTime,
...message
})),
["from", "to"]
);
This pattern allows you to fully customize the shape and content of each item in the stream before it is returned or merged with other streams Streamed Joins (flatMap).