Bumps ssri from 6.0.1 to 6.0.2. <details> <summary>Changelog</summary> <p><em>Sourced from <a href="https://github.com/npm/ssri/blob/v6.0.2/CHANGELOG.md">ssri's changelog</a>.</em></p> <blockquote> <h2><a href="https://github.com/zkat/ssri/compare/v6.0.1...v6.0.2">6.0.2</a> (2021-04-07)</h2> <h3>Bug Fixes</h3> <ul> <li>backport regex change from 8.0.1 (<a href="https://github.com/zkat/ssri/commit/b30dfdb">b30dfdb</a>), closes <a href="https://github-redirect.dependabot.com/zkat/ssri/issues/19">#19</a></li> </ul> <p></p> </blockquote> </details> <details> <summary>Commits</summary> <ul> <li><a href="https://github.com/npm/ssri/commit/b7c8c7c61db89aeb9fbf7596c0ef17071bc216ef"><code>b7c8c7c</code></a> chore(release): 6.0.2</li> <li><a href="https://github.com/npm/ssri/commit/b30dfdb00bb94ddc49a25a85a18fb27afafdfbb1"><code>b30dfdb</code></a> fix: backport regex change from 8.0.1</li> <li>See full diff in <a href="https://github.com/npm/ssri/compare/v6.0.1...v6.0.2">compare view</a></li> </ul> </details> <details> <summary>Maintainer changes</summary> <p>This version was pushed to npm by <a href="https://www.npmjs.com/~nlf">nlf</a>, a new releaser for ssri since your current version.</p> </details> <br />

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PR opened spotify/scio

Use the same hash fn in hashPartition and hashPartitionByKey

Just noticed while reviewing the code that the hash functions used for partitioning the lhs and the rhs can have slightly different behaviors in transforms like sparseIntersectByKey. Not sure if there is a practical implication of this.

+13 -10

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Neville Li

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fix sparse transforms split on the wrong side (#3770) regression from https://github.com/spotify/scio/commit/a824db3bf0#diff-e957bccad978e20a8db993e00bf89c894f82521d980de57684402176351fd5b6L492-R435

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fix sparse transforms split on the wrong side

regression from https://github.com/spotify/scio/commit/a824db3bf0#diff-e957bccad978e20a8db993e00bf89c894f82521d980de57684402176351fd5b6L492-R435

I don't think it affect correctness. By using the potentially much larger LHS keys by mistake, we're saturating the BFs so that they'll return a lot of false positives, but they'll be filtered out in the exact join of "hot" sides. The only problem is it's not keeping enough "chill" keys from shuffling (BF return false, i.e. key not in RHS).

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nevillelyh

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pull request commentspotify/scio

fix sparse transforms split on the wrong side

@anish749 it was my copy/pasta error. Test didn't catch it since the result is always correct and data size was too small to reveal any issue.

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pull request commentspotify/magnolify

Update munit-scalacheck to 0.7.25

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PR opened spotify/magnolify

Update munit-scalacheck to 0.7.25

Updates org.scalameta:munit-scalacheck from 0.7.23 to 0.7.25. GitHub Release Notes - Version Diff

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Neville Li

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update docs: Algebird => Guava BloomFilter

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Pull request review commentspotify/apollo

Make a HEAD /foo requests emit metrics for HEAD:/foo, not GET:/foo

   @Override   public EndpointRunnableFactory apply(EndpointRunnableFactory delegate) {     return (request, requestContext, endpoint) -> {-      final String endpointName = endpoint.info().getName();+      final String endpointName;+      // Normally request.request().method() and endpoint.info().getRequestMethod() are equal+      // but since apollo lets GET endpoints also serve HEAD requests, they are different+      // when the request is a HEAD request. The hack below makes sure we distinguish metrics+      // for GET and HEAD requests.+      // We could always take the else branch but I'm guessing that the comparison+      // is cheaper than a needless concatenation.+      if (request.request().method().equals(endpoint.info().getRequestMethod())) {+        endpointName = endpoint.info().getName();+      } else {+        // but because GET endpoints serve HEAD requests info().getName() is+        // wrong does not distinguish HEAD and GET requests+        endpointName = request.request().method() + ":" + endpoint.info().getUri();

Looks like this could work. But it would be more elegant if proper endpoint infos were set up for the implicit HEAD endpoints. This is a bit of a hack, isn't it?

mikaelgoldmann

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Pull request review commentspotify/semantic-metrics

Document Distributions

 The default histogram implementation uses a random sampling algorithm with expon This was authored by Johan Buratti.   +## Distribution+An aggregatable data structure, T-digest, that allows for aggregating percentiles. To get accurate percentiles, you need to setup good bucket boundaries.

Thanks for sharing that @ao2017 I used it to re-write the first part. Should be good for another review now

lmuhlha