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Fixes to 'Best Practices for Single Row Lookups' post #213

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Fixes to 'Best Practices for Single Row Lookups' post #213

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8 changes: 4 additions & 4 deletions content/blog/2026-10-24-point-queries.md
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Expand Up @@ -67,9 +67,9 @@ from generate_series(1, {rows}) t(i)
Unless otherwise stated, the queries tested retrieve a random row from the table
by key (i.e., `select v from t where k = {}`). Most tests run on a
`c4d-standard-4` (4 vCPUs, 15GB RAM) Google Cloud virtual machine, using a 100GB
[HyperDisk] (6k IOPS, 500 MB/s) to store the data. The [Runtime](#runtime) test
uses a machine with more CPU cores (`c4d-highcpu-8`). Each test builds on the
last.
[HyperDisk] (6k IOPS, 500 MB/s) to store the data. The [Target
Partitions](#target-partitions) and [Runtime](#runtime) tests use a machine with
more CPU cores (`c4d-highcpu-8`). Each test builds on the last.

[HyperDisk]: https://docs.cloud.google.com/compute/docs/disks/hyperdisks

Expand Down Expand Up @@ -273,7 +273,7 @@ page size (y).
<b>Figure 9</b>: Throughput based on the read size and the size of the
Parquet data pages (512k rows per row group). Each cell shows the ratio
between its throughput value and the max throughput in its column
(darker is better)..
(darker is better).
</p>
</div>

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