#Query cache

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atomic scarabBOT
#

Hi @whole marlin. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:

#

While the approach you've shown can work for caching Convex data in Next.js, it's not the recommended pattern when using Convex. Convex provides its own caching and real-time update mechanisms that are more efficient and easier to use. Let me explain why:

  1. Automatic caching: Convex automatically caches query results and keeps them up-to-date in real-time. As mentioned in the Realtime documentation (https://docs.convex.dev/realtime), "Convex automatically caches the result of your query functions so that future calls just read from the cache. The cache is updated if the data ever changes."

  2. Real-time updates: Convex queries are automatically reactive. When data changes, Convex will push updates to your clients without you having to manually invalidate caches or revalidate data.

  3. Server-side rendering: For Next.js server-side rendering with Convex, the recommended approach is to use preloadQuery and usePreloadedQuery. This allows you to fetch data on the server and then seamlessly transition to real-time updates on the client. You can find more details in the Next.js Server Rendering documentation (https://docs.convex.dev/client/react/nextjs/server-rendering).

  4. Client-side caching: If you need additional caching on the client side, Convex provides a ConvexQueryCacheProvider that can keep query subscriptions active even when components unmount. This is explained in a Stack article on magic caching (https://stack.convex.dev/magic-caching).

Instead of using unstable_cache, a more Convex-friendly approach for Next.js would be:

import { preloadQuery } from "convex/nextjs";
import { api } from "@/convex/_generated/api";
#

export async function MashupWrapper({ mashupId }) {
const preloadedMashup = await preloadQuery(api.mashups.getMashupsByIds, {
mashupIds: [mashupId]
});
return <Mashup preloadedMashup={preloadedMashup} />;
}


Then in your client component:

```typescript
"use client";

import { Preloaded, usePreloadedQuery } from "convex/react";
import { api } from "@/convex/_generated/api";

export function Mashup(props: {
  preloadedMashup: Preloaded<typeof api.mashups.getMashupsByIds>;
}) {
  const mashup = usePreloadedQuery(props.preloadedMashup);
  // render mashup...
}

This approach leverages Convex's built-in caching and real-time capabilities while still allowing for efficient server-side rendering in Next.js. It's generally more efficient and easier to maintain than manual caching solutions.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@whole marlin Kapa.ai is still learning and improving, please let me know how I did by reacting below.

whole marlin
#

will this query run everytime a user navigates to this page? even if its not the 1st load?

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@atomic scarab The goal is for the query to run once during the initial page load and it this query shouldn't be called again for a while as the data doesn't change much. Howver using preloadedQuery i realize it uses no-store cache policy so even after the 1st load, the query is called

atomic scarabBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

I understand your goal is to have the query run once during the initial page load and then not be called again for a while, since the data doesn't change frequently. You're correct that preloadQuery uses a 'no-store' cache policy, which can lead to more frequent server calls than you desire. Let's explore some alternatives that might better suit your needs:

  1. Using fetchQuery:
    Instead of preloadQuery, you can use fetchQuery in your server component. This approach will fetch the data once during server-side rendering and won't automatically revalidate on the client side. Here's how you can implement it:
import { fetchQuery } from "convex/nextjs";
import { api } from "@/convex/_generated/api";

export async function MashupWrapper({ mashupId }) {
  const mashup = await fetchQuery(api.mashups.getMashupsByIds, {
    mashupIds: [mashupId]
  });
  return <Mashup mashup={mashup} />;
}

This method will fetch the data once during the initial page load, and the data won't be automatically updated. However, be aware that this approach is not reactive, as mentioned in the Next.js Server Rendering documentation (https://docs.convex.dev/client/react/nextjs/server-rendering#consistency):

"What does it mean when it says that it will not be reactive when using fetchQuery?"

  1. Client-side caching:
    If you want to maintain some level of reactivity while reducing the number of server calls, you can use the ConvexQueryCacheProvider from the convex-helpers package. This will keep the query subscription active even when components unmount, reducing the need for refetching. Here's how you can use it:
import { ConvexQueryCacheProvider } from "convex-helpers/react/cache";
#

function MyApp({ Component, pageProps }) {
return (
<ConvexProvider client={convex}>
<ConvexQueryCacheProvider>
<Component {...pageProps} />
</ConvexQueryCacheProvider>
</ConvexProvider>
);
}


This approach is described in the **Stack article on magic caching** (<https://stack.convex.dev/magic-caching>).

3. Custom caching solution:
If you need more control over when the data is refetched, you could implement a custom caching solution using React's `useState` and `useEffect` hooks, along with `localStorage` for persistence across page reloads. This would allow you to fetch the data once and then only refetch after a certain time has passed.

Remember, while these solutions can reduce the number of server calls, they also introduce the possibility of serving stale data. You'll need to balance the trade-off between data freshness and server load based on your specific use case.

If none of these solutions fully meet your needs, you might want to consider reaching out to the Convex team for more tailored advice on your specific use case.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
#

@whole marlin Kapa.ai is still learning and improving, please let me know how I did by reacting below.

whole marlin
#

I believe the fetchQuery also uses a no-store cache policy

#

@atomic scarab looks like the fetchQuery also uses a no-store cache policy

atomic scarabBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

You're correct, and I apologize for not clarifying that earlier. Both preloadQuery and fetchQuery in Convex indeed use a 'no-store' cache policy when used with Next.js server-side rendering. This is mentioned in the Next.js Server Rendering documentation (https://docs.convex.dev/client/react/nextjs/server-rendering#consistency):

"preloadQuery (https://docs.convex.dev/api/modules/nextjs#preloadquery) and fetchQuery (https://docs.convex.dev/api/modules/nextjs#fetchQuery) use the ConvexHTTPClient under the hood. This client is stateless. This means that two calls to preloadQuery are not guaranteed to return consistent data based on the same database state."

Given this constraint, if you need to implement caching for data that doesn't change frequently, you might need to consider alternative approaches:

  1. Client-side caching: As mentioned before, you could use the ConvexQueryCacheProvider from the convex-helpers package. This keeps query subscriptions active even when components unmount, reducing refetches.

  2. Custom caching layer: You could implement a custom caching layer in your Next.js app, outside of Convex. This might involve using Next.js API routes or middleware to cache the results of Convex queries.

  3. Tanstack Query (React Query): Convex has integration with Tanstack Query, which provides powerful caching capabilities. You can find more information about this in the Tanstack Query documentation for Convex (https://docs.convex.dev/client/tanstack-query).

#
  1. Convex's built-in caching: Remember that Convex does have its own caching mechanism on the server side. As mentioned in the Queries that scale (https://stack.convex.dev/queries-that-scale#3-optimizing-queries-for-caching) article, "Convex manages caching for you, along with invalidating the cache and updating your UI whenever you have a subscription, such as using the useQuery React hook."

If these solutions don't fully address your needs, you might want to reach out to the Convex team directly for more specific advice on your use case. They might be able to provide more tailored solutions or upcoming features that could help with your specific caching requirements.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@whole marlin Kapa.ai is still learning and improving, please let me know how I did by reacting below.

whole marlin
#

@atomic scarab so does doing the following make sense for our problem? const getCachedMashup = unstable_cache( async (mashupId) => { return await fetchQuery(api.mashups.getMashupsByIds, { mashupIds: [mashupId] }); }, ['mashup-detail', mashupId], // Correct syntax: direct array, not a function { revalidate: 3600, tags: [`mashup-${mashupId}`] } );

atomic scarabBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

While the approach you've suggested using unstable_cache with fetchQuery can work to cache Convex data in Next.js, it's not the recommended pattern when using Convex. Here's why:

  1. Redundant Caching: Convex already provides its own caching mechanism. As mentioned in the Realtime documentation (https://docs.convex.dev/realtime), "Convex automatically caches the result of your query functions so that future calls just read from the cache."

  2. Potential Inconsistencies: Using unstable_cache on top of Convex's fetchQuery might lead to inconsistencies between the Next.js cache and Convex's internal cache.

  3. Loss of Reactivity: This approach loses the real-time update capabilities that Convex provides out of the box.

  4. Complexity: It adds an extra layer of complexity that might not be necessary given Convex's built-in features.

Instead of using unstable_cache, consider leveraging Convex's built-in caching and real-time capabilities. If you need to optimize for less frequent data fetching, you might want to look into:

  1. Using the ConvexQueryCacheProvider from the convex-helpers package, as mentioned in the Stack article on magic caching (https://stack.convex.dev/magic-caching).

  2. Implementing a custom caching solution using React's state management and localStorage, if you need more control over when data is refetched.

  3. Exploring Convex's integration with Tanstack Query (React Query) for more advanced caching strategies, as described in the Tanstack Query documentation for Convex (https://docs.convex.dev/client/tanstack-query).

#

Remember, Convex is designed to handle caching and real-time updates efficiently. If you're finding that you need to implement additional caching layers, it might be worth revisiting your data model or query structure to ensure you're making the most of Convex's built-in optimizations.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@whole marlin Kapa.ai is still learning and improving, please let me know how I did by reacting below.

whole marlin
#

@atomic scarab is this valid convex syntax? ``` // await ctx.scheduler.runAction(api.actions.revalidateCache);

atomic scarabBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

The syntax you've provided is not valid for Convex. The correct way to schedule an action using the Convex scheduler is slightly different. Here's the correct syntax:

await ctx.scheduler.runAfter(0, api.actions.revalidateCache);

This syntax is based on the Scheduler interface documentation (https://docs.convex.dev/api/interfaces/server.Scheduler) from Convex. The runAfter method is used to schedule a function to execute after a specified delay. In this case, using 0 as the delay means the action will run immediately after the current function completes.

Some key points to note:

  1. Convex uses runAfter instead of runAction.
  2. The first argument is the delay in milliseconds.
  3. The second argument is the function reference.

If you need to pass arguments to the action, you can do so as a third argument:

await ctx.scheduler.runAfter(0, api.actions.revalidateCache, { someArg: "value" });

Remember that scheduling actions from mutations is atomic with the rest of the mutation, as mentioned in the Scheduled Functions documentation (https://docs.convex.dev/scheduling/scheduled-functions#scheduling-from-mutations). This means if the mutation succeeds, the scheduled action is guaranteed to be scheduled.

If you're trying to call an action immediately from within another action (not scheduling it), you would use ctx.runAction instead:

await ctx.runAction(api.actions.revalidateCache);

This is described in the Action context documentation (https://docs.convex.dev/functions/actions#action-context).

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@whole marlin Kapa.ai is still learning and improving, please let me know how I did by reacting below.

whole marlin
#

@atomic scarab is this a good idea? ```// Define the cached fetch function
const getCachedMashup = unstable_cache(
async (mashupId) => {
return await fetchQuery(api.mashups.getMashupsByIds, {
mashupIds: [mashupId]
});
},
['mashup-detail'], // Base cache key
{
revalidate: 300_000, // Cache for 5 minutes
tags: ['mashup']
}
);

export default async function MashupDetailsPage({ params: { mashupId } }) {
// Use the cached fetch
const preloadedMashup = await getCachedMashup(mashupId);

return (
    <ClientMashupDetails 
        mashup={preloadedMashup[0]} 
        mashupIds={mashupId} 
    />
);

}``` I notice the fetchquery is called on every page load even if its not the first load.

atomic scarabBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

While the approach you've shown can work, it's not the recommended pattern when using Convex with Next.js. Let me explain why and suggest some alternatives:

  1. Redundant Caching: As mentioned in the Realtime documentation (https://docs.convex.dev/realtime), "Convex automatically caches the result of your query functions so that future calls just read from the cache." By using unstable_cache, you're adding an additional layer of caching on top of Convex's built-in caching mechanism, which may lead to unnecessary complexity.

  2. Loss of Reactivity: This approach loses the real-time update capabilities that Convex provides out of the box. Convex is designed to keep your data up-to-date automatically.

  3. Inconsistent Behavior: You've noticed that fetchQuery is called on every page load, even if it's not the first load. This is because fetchQuery uses a 'no-store' cache policy when used with Next.js server-side rendering, as mentioned in the Next.js Server Rendering documentation (https://docs.convex.dev/client/react/nextjs/server-rendering#consistency).

Instead of using unstable_cache with fetchQuery, consider these alternatives:

  1. Use preloadQuery and usePreloadedQuery:
    This is the recommended approach for server-side rendering with Convex in Next.js. It allows you to fetch data on the server and then seamlessly transition to real-time updates on the client.

    import { preloadQuery } from "convex/nextjs";
import { api } from "@/convex/_generated/api";

export default async function MashupDetailsPage({ params: { mashupId } }) {
  const preloadedMashup = await preloadQuery(api.mashups.getMashupsByIds, { 
    mashupIds: [mashupId]
  });
  return <ClientMashupDetails preloadedMashup={preloadedMashup} mashupId={mashupId} />;
}

    Then in your client component:

# 
   "use client";
   import { usePreloadedQuery } from "convex/react";

   export function ClientMashupDetails({ preloadedMashup, mashupId }) {
     const mashup = usePreloadedQuery(preloadedMashup);
     // render mashup...
   }

  1. Use Convex's integration with Tanstack Query:
    If you need more control over caching, consider using Tanstack Query with Convex. This provides powerful caching capabilities while still leveraging Convex's real-time features. You can find more information in the Tanstack Query documentation for Convex (https://docs.convex.dev/client/tanstack-query).

  2. Use ConvexQueryCacheProvider:
    If you want to reduce the number of server calls while maintaining some level of reactivity, you can use the ConvexQueryCacheProvider from the convex-helpers package. This keeps query subscriptions active even when components unmount, as described in the Stack article on magic caching (https://stack.convex.dev/magic-caching).

Remember, Convex is designed to handle caching and real-time updates efficiently. If you find yourself needing to implement additional caching layers, it might be worth revisiting your data model or query structure to ensure you're making the most of Convex's built-in optimizations.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

whole marlin
#

I like option 1 however the server is called everytime I navigate to the page.

#

@atomic scarab I like option 1 however the server is called everytime I navigate to the page.

atomic scarabBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

I understand your concern about the server being called every time you navigate to the page when using the preloadQuery approach. This behavior is indeed by design, as explained in the Next.js Server Rendering documentation (https://docs.convex.dev/client/react/nextjs/server-rendering#preloading-data-for-client-components):

"preloadQuery uses the cache: 'no-store' policy so any Server Components using it will not be eligible for static rendering."

This means that the query will be executed on every page load to ensure fresh data. However, if your data doesn't change frequently and you want to reduce server calls, we can explore a few alternatives:

  1. Use fetchQuery with Next.js caching:
    You can use fetchQuery in combination with Next.js's cache function to cache the result on the server side:

    import { cache } from 'react';
import { fetchQuery } from "convex/nextjs";
import { api } from "@/convex/_generated/api";

const getCachedMashup = cache(async (mashupId: string) => {
  return await fetchQuery(api.mashups.getMashupsByIds, { 
    mashupIds: [mashupId]
  });
});

export default async function MashupDetailsPage({ params: { mashupId } }) {
  const mashup = await getCachedMashup(mashupId);
  return <ClientMashupDetails mashup={mashup[0]} mashupId={mashupId} />;
}

    This approach will cache the result on the server, reducing the number of calls to Convex. However, be aware that this loses the real-time update capabilities of Convex.

#

  1. Use ConvexQueryCacheProvider:
    As mentioned in previous responses, you can use the ConvexQueryCacheProvider from the convex-helpers package to keep query subscriptions active even when components unmount. This can reduce the number of server calls while maintaining some level of reactivity. You can find more information about this in the Stack article on magic caching (https://stack.convex.dev/magic-caching).

  2. Implement a custom caching solution:
    You could implement a custom caching solution using React's useState and useEffect hooks, along with localStorage for persistence across page reloads. This would allow you to fetch the data once and then only refetch after a certain time has passed.

Remember, while these solutions can reduce the number of server calls, they also introduce the possibility of serving stale data. You'll need to balance the trade-off between data freshness and server load based on your specific use case.

If none of these solutions fully meet your needs, you might want to consider reaching out to the Convex team for more tailored advice on your specific use case. They might be able to provide more specific guidance or information about upcoming features that could help with your particular requirements.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@whole marlin Kapa.ai is still learning and improving, please let me know how I did by reacting below.

whole marlin
#

@atomic scarab In option 1 should I use cache from react or { unstable_cache } from 'next/cache';

atomic scarabBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

For option 1, you should use { unstable_cache } from 'next/cache' instead of cache from React. Here's why:

  1. Next.js-specific caching: unstable_cache is a Next.js-specific function designed to work with server-side rendering and data fetching in Next.js applications. It provides more control over caching behavior in the context of Next.js server components.

  2. More flexible caching options: unstable_cache allows you to specify revalidation periods and tags, which can be useful for more granular control over when your cached data should be refreshed.

Here's how you can modify the example to use unstable_cache:

import { unstable_cache } from 'next/cache';
import { fetchQuery } from "convex/nextjs";
import { api } from "@/convex/_generated/api";

const getCachedMashup = unstable_cache(
  async (mashupId: string) => {
    return await fetchQuery(api.mashups.getMashupsByIds, { 
      mashupIds: [mashupId]
    });
  },
  ['mashup-detail'],
  { revalidate: 60 } // Cache for 60 seconds, adjust as needed
);

export default async function MashupDetailsPage({ params: { mashupId } }) {
  const mashup = await getCachedMashup(mashupId);
  return <ClientMashupDetails mashup={mashup[0]} mashupId={mashupId} />;
}

However, it's important to note that while this approach can reduce the number of calls to Convex, it comes with some trade-offs:

  1. Loss of real-time updates: As mentioned in the Next.js Server Rendering documentation (https://docs.convex.dev/client/react/nextjs/server-rendering#consistency), this approach loses Convex's real-time update capabilities.

  2. Potential for stale data: Depending on your caching settings, there's a possibility of serving stale data to users.

  3. Complexity: This adds an extra layer of caching on top of Convex's built-in caching mechanism, which might introduce unnecessary complexity.

#

Given these considerations, you should carefully evaluate whether this caching approach aligns with your application's requirements for data freshness and real-time updates. If real-time updates are important for your use case, you might want to reconsider using Convex's built-in features or explore other options we discussed earlier.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@whole marlin Kapa.ai is still learning and improving, please let me know how I did by reacting below.

whole marlin
#

when using fetchQuery on localhost it seems to not make extra requests to the server after the first load but in production. But when using my convex dev and vercel prod it makes the request on every load

#

@atomic scarab when using fetchQuery on localhost it seems to not make extra requests to the server after the first load but in production. But when using my convex dev and vercel prod it makes the request on every load

atomic scarabBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

This behavior difference between localhost and production environments when using fetchQuery is indeed an interesting observation. Let's break down what might be happening:

  1. Localhost behavior:
    On localhost, Next.js development server might be caching the results of fetchQuery calls, which is why you're not seeing extra requests after the first load. This caching behavior in development mode can sometimes differ from production behavior.

  2. Production behavior:
    In production, particularly when using Vercel, fetchQuery is making requests on every load. This aligns with what's mentioned in the Next.js Server Rendering documentation (https://docs.convex.dev/client/react/nextjs/server-rendering#consistency):

"fetchQuery (https://docs.convex.dev/api/modules/nextjs#fetchQuery) use[s] the ConvexHTTPClient under the hood. This client is stateless. This means that two calls to preloadQuery are not guaranteed to return consistent data based on the same database state."

The production behavior you're seeing is actually the expected behavior for fetchQuery in a server-side rendering context. It's designed to always fetch fresh data to ensure consistency.

To address this issue and reduce the number of requests in production, you have a few options:

  1. Use unstable_cache from Next.js:
    As discussed earlier, you can wrap your fetchQuery call with unstable_cache to add a caching layer. However, this comes with trade-offs in terms of real-time updates and potential for stale data.

  2. Implement client-side caching:
    You could use the ConvexQueryCacheProvider from the convex-helpers package to keep query subscriptions active even when components unmount. This is described in the Stack article on magic caching (https://stack.convex.dev/magic-caching).

#

  1. Use Tanstack Query with Convex:
    Convex has integration with Tanstack Query, which provides powerful caching capabilities. You can find more information about this in the Tanstack Query documentation for Convex (https://docs.convex.dev/client/tanstack-query).

  2. Revisit your data fetching strategy:
    Consider if you really need server-side rendering for this data. If not, you could move the data fetching to the client side using useQuery, which would leverage Convex's built-in caching and real-time update features.

Remember, while reducing server requests is important, it's equally crucial to ensure your app always has the most up-to-date data. The approach you choose should balance these concerns based on your specific use case.

If you're still unsure about the best approach for your situation, it might be worth reaching out to the Convex support team for more tailored advice.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@whole marlin Kapa.ai is still learning and improving, please let me know how I did by reacting below.

whole marlin
#

@atomic scarab can i create an index like this on a table .index("by_all_filters", [ "visibility", "region", "genre", "key", "variantName", "bpm" ]) and if so when i run a query, do i need to include all the fields? what if I don't care about genre? only the other filters, would I need to create a diffrent index for that case? Basically these filters are all optional

atomic scarabBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

Yes, you can create an index like that in Convex. This is called a compound index, and it's a valid way to define an index on multiple fields. Here's what you need to know about using such an index:

  1. Index Creation: The syntax you've shown is correct for creating a compound index. As described in the Convex documentation on indexes (https://docs.convex.dev/database/indexes), you can define an index on multiple fields using the .index() method.

  2. Querying with the Index: When querying using this index, you don't need to include all the fields. However, there's an important rule to follow: you must specify the fields in order from left to right without skipping any. This is explained in the Introduction to Indexes and Query Performance (https://docs.convex.dev/database/indexes/indexes-and-query-perf#indexing-multiple-fields) documentation.

  3. Optional Filters: If some of your filters are optional (as you mentioned), you have a few options:

    a. Create multiple indexes: You could create different indexes for different combinations of filters. However, this can lead to a large number of indexes, which isn't ideal.

    b. Use a single index, but query creatively: You can use the index you've defined, but you'll need to structure your queries carefully. For example:

    .withIndex("by_all_filters", (q) =>
  q.eq("visibility", visibilityFilter)
   .eq("region", regionFilter)
   .gte("genre", "") // This effectively ignores the genre filter
   .eq("key", keyFilter)
   .eq("variantName", variantNameFilter)
   .gte("bpm", 0) // This effectively ignores the BPM filter
)

    In this example, we're using .gte("") or .gte(0) for optional filters we don't care about. This allows us to use the index without actually filtering on those fields.

#
  1. Performance Considerations: Remember that the performance of your query is based on how many documents are in the index range, as mentioned in the Convex Query Performance documentation (https://stack.convex.dev/convex-query-performance#indexing-multiple-fields).

Given that your filters are all optional, you might want to consider creating a few different indexes based on the most common filter combinations, rather than one large compound index. This could provide better performance for various query patterns.

For example:

.index("by_visibility_region", ["visibility", "region"])
.index("by_genre_key", ["genre", "key"])
.index("by_bpm", ["bpm"])

Then, depending on which filters are active in a given query, you can choose the most appropriate index.

Remember, the choice of indexes should be based on your most common and performance-critical query patterns. If you're unsure about the best indexing strategy for your specific use case, you might want to reach out to the Convex team for more tailored advice.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@whole marlin Kapa.ai is still learning and improving, please let me know how I did by reacting below.

whole marlin
#

can I use more than 1 .gte in a query?

#

@atomic scarab can I use more than 1 .gte in a query?

atomic scarabBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

To answer your question about using more than one .gte in a query, we need to consider how Convex handles index range expressions. According to the Convex documentation on querying documents using indexes (https://docs.convex.dev/database/indexes#querying-documents-using-indexes), there are specific rules for constructing index range expressions:

  1. You can have 0 or more equality expressions defined with .eq.
  2. Optionally, you can have a lower bound expression defined with .gt or .gte.
  3. Optionally, you can have an upper bound expression defined with .lt or .lte.

Importantly, the documentation states: "You must step through fields in index order."

This means that you cannot use more than one .gte in a single index range expression. The structure of an index range expression is designed to work with how indexes are organized in Convex, which is similar to how data would be sorted in a spreadsheet.

Here's an example of a valid query using .gte:

const messages = await ctx.db
  .query("messages")
  .withIndex("by_channel", (q) =>  
    q
      .eq("channel", channel)
      .gte("_creationTime", Date.now() - 2 * 60000)
  )
  .collect();

If you need to apply multiple lower bounds, you have a few options:

  1. Use a combination of .eq and .gte on different fields, in index order.
  2. Use .gte on one field and then apply additional filtering using the .filter() method, which allows for more complex conditions but may be less efficient.
  3. Consider restructuring your data model or creating a different index that better suits your query needs.
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Remember, the performance of your query is based on the specificity of the range you define in the index expression. If you find yourself needing multiple .gte conditions, it might be worth revisiting your data model or index structure to ensure you're making the most efficient use of Convex's querying capabilities.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

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@whole marlin Kapa.ai is still learning and improving, please let me know how I did by reacting below.

whole marlin
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@atomic scarab how efficient is this table? I have about 10,000 mashupVariants mashupVariants: defineTable({ mashupId: v.id("mashups"), name: v.string(), // variant name mashupTitle: v.string(), // denormalized from mashup key: v.string(), // denormalized from mashup genre: v.string(), // denormalized from mashup bpm: v.number(), // denormalized from mashup visibility: v.string(), // denormalized from mashup releaseDate: v.number(), // denormalized from mashup region: v.string(), // new region field }) // Original indexes .index("by_name_key_genre_visibility_bpm", [ "name", "key", "genre", "visibility", "bpm", ]) .index("by_name_key_visibility_bpm", ["name", "key", "visibility", "bpm"]) .index("by_name_visibility_bpm", ["name", "visibility", "bpm"]) .index("by_name_genre_visibility_bpm", [ "name", "genre", "visibility", "bpm", ]) .index("by_genre_visibility_bpm", ["genre", "visibility", "bpm"]) .index("by_key_genre_visibility_bpm", ["key", "genre", "visibility", "bpm"]) .index("by_key_visibility_bpm", ["key", "visibility", "bpm"]) .index("by_visibility_bpm", ["visibility", "bpm"]) .index("by_mashupId", ["mashupId"]) // New region-based indexes .index("by_region_visibility_bpm", ["region", "visibility", "bpm"]) .index("by_region_genre_visibility_bpm", ["region", "genre", "visibility", "bpm"]) .index("by_region_key_visibility_bpm", ["region", "key", "visibility", "bpm"]) .index("by_region_name_visibility_bpm", ["region", "name", "visibility", "bpm"]),

atomic scarabBOT
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Let me check the knowledge base for you again. One moment! :books:

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Based on the table structure and indexes you've provided, I can offer some insights on its efficiency:

  1. Number of Indexes: Your table has 13 indexes, which is a significant number. As mentioned in the Convex documentation on indexes (https://docs.convex.dev/database/indexes/indexes-and-query-perf#backfilling-and-maintaining-indexes), "As you define more indexes, the cost to maintain them grows because every insert needs to update every index." Convex has a limit of 32 indexes per table, so you're well within that limit, but it's still a considerable number.

  2. Denormalization: You've denormalized several fields from the mashups table into the mashupVariants table. This can improve query performance by reducing the need for joins, but it also increases data redundancy and the complexity of maintaining data consistency.

  3. Compound Indexes: You're making good use of compound indexes, which can be very efficient for queries that use multiple fields. As stated in the Convex documentation on query performance (https://stack.convex.dev/queries-that-scale#how-much-indexing-is-too-much), "What you might find surprising, however, is that a multi-field index doesn't incur this overhead. So sharing a multi-field index should be done whenever possible."

  4. Index Order: The order of fields in your indexes is important. As per the Convex documentation on indexing multiple fields (https://docs.convex.dev/database/indexes/indexes-and-query-perf#indexing-multiple-fields), "You must step through fields in index order." Your indexes seem to be designed to support various query patterns, which is good.

  5. Table Size: With about 10,000 mashupVariants, your table is of a moderate size. At this scale, having well-designed indexes is important for maintaining good query performance.

However, there are some potential concerns:

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  1. Overlapping Indexes: Some of your indexes overlap significantly (e.g., "by_visibility_bpm" is a subset of several other indexes). This might lead to unnecessary duplication.

  2. Write Performance: With 13 indexes, each write operation (insert/update/delete) will need to update all these indexes, which could impact write performance.

  3. Flexibility: The large number of specific indexes might make it challenging to efficiently support new query patterns that don't match these exact index structures.

To optimize this table structure, consider:

  1. Analyzing your most common and performance-critical query patterns. Ensure you have indexes that support these efficiently.

  2. Considering if you can consolidate some indexes. For example, you might be able to use a single index for queries that currently use "by_visibility_bpm" and "by_genre_visibility_bpm".

  3. Evaluating if all these denormalized fields are necessary, or if some could be kept in the mashups table and fetched when needed.

  4. Monitoring the performance of your queries and write operations in production to identify any bottlenecks.

Remember, as stated in the Convex documentation on best practices (https://docs.convex.dev/production/best-practices/#database), "Use indexes or paginate all large database queries." Ensure you're using these indexes effectively in your queries and consider implementing pagination for large result sets.

If you're unsure about the optimal index structure for your specific use case, it might be worth reaching out to the Convex support team for more tailored advice.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon: