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11 changes: 11 additions & 0 deletions src/content/changelogs/d1.yaml
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Expand Up @@ -5,6 +5,17 @@ productLink: "/d1/"
productArea: Developer platform
productAreaLink: /workers/platform/changelog/platform/
entries:
- publish_date: "2025-01-13"
title: D1 will begin enforcing its free tier limits from the 10th of February 2025.
description: |-
D1 will begin enforcing the daily [free tier limits](/d1/platform/limits) from 2025-02-10. These limits only apply to accounts on the Workers Free plan.

From 2025-02-10, if you do not take any action and exceed the daily free tier limits, queries to D1 databases via the Workers API and/or REST API will return errors until limits reset daily at 00:00 UTC.

To ensure uninterrupted service, upgrade your account to the [Workers Paid plan](/workers/platform/pricing/) from the [plans page](https://dash.cloudflare.com/?account=/workers/plans). The minimum monthly billing amount is $5. Refer to [Workers Paid plan](/workers/platform/pricing/) and [D1 limits](/d1/platform/limits/).

For better insight into your current usage, refer to your [billing metrics](/d1/observability/billing/) for rows read and rows written, which can be found on the [D1 dashboard](https://dash.cloudflare.com/?account=/workers/d1) or GraphQL API.

- publish_date: "2024-08-23"
title: D1 alpha databases have stopped accepting SQL queries
description: |-
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2 changes: 1 addition & 1 deletion src/content/docs/d1/platform/limits.mdx
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Expand Up @@ -25,7 +25,7 @@ import { Render } from "~/components";
| Maximum characters (bytes) in a `LIKE` or `GLOB` pattern | 50 bytes |
| Maximum bindings per Workers script | Approximately 5,000 [^2] |
| Maximum SQL query duration | 30 seconds [^3] |
| Maximum file import (`d1 execute`) size | 5 GiB [^4] |
| Maximum file import (`d1 execute`) size | 5 GB [^4] |

:::note[Batch limits]
Limits for individual queries (listed above) apply to each individual statement contained within a batch statement. For example, the maximum SQL statement length of 100 KB applies to each statement inside a `db.batch()`.
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