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marvin-j97 committed Jan 29, 2024
2 parents 818f5c7 + 8a5f548 commit 24aaa23
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2 changes: 1 addition & 1 deletion docs/src/content/docs/guides/column-keys.md
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Expand Up @@ -17,5 +17,5 @@ Column qualifiers in Bigtable can be arbitrary byte arrays. In Smoltable, they n
## Default column

The column qualifier can be omitted to access the default column (empty). For instance,
we wanted to have a single column family that stores a title, the column would be accessed
if we wanted to have a single column family that stores a title, the column would be accessed
by `title:`. In that case, the `:` can be omitted: `title`.
4 changes: 2 additions & 2 deletions docs/src/content/docs/guides/locality-groups.md
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Expand Up @@ -114,7 +114,7 @@ All data is stored in the `_dat_scan-example` partition.

Let's ingest some data and query it (body is truncated for brevity):

```json
```bash
curl --request POST \
--url http://localhost:9876/v1/table/scan-example/write \
--header 'content-type: application/json' \
Expand Down Expand Up @@ -372,7 +372,7 @@ which returns (truncated):
}
```

We get the exact same result, however, we reduce scanned bytes down to 680 bytes, and halved scanned cells, and achieved a read amplification of `1`!
We get the exact same result, however, we reduced scanned bytes down to 680 bytes, and halved scanned cells, achieving a read amplification of `1`!

## Example: Scanning another column family

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2 changes: 1 addition & 1 deletion docs/src/content/docs/guides/wide-column-intro.md
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Expand Up @@ -61,7 +61,7 @@ restructure our data:
| row key | flight\:FI318 | flight\:FI319 | flight\:EW7033 | flight\:EW7036 | meta\:miles | meta\:model | meta\:operator |
| --- | --- | --- | --- | --- | --- | --- | --- |
| plane#TF-FIR | 2024-01-25 | 2024-01-25 | | | 51000000 | Boeing 757-256 | Icelandair |
| plane#plane#D-AIQN | | | 2019-10-31 | 2019-10-31 | 52142142 | Airbus A320-211 | Germanwings |
| plane#D-AIQN | | | 2019-10-31 | 2019-10-31 | 52142142 | Airbus A320-211 | Germanwings |

We can easily store all data in a single row, using two column families, `flight` and `meta`. Meta contains arbitrary columns
of different kind of metadata, while each column inside `flights` is a flight number, with the cell value being the flight data.
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