Remove linear qualification
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@ -319,8 +319,8 @@ metadata engine and thus focus only on 16-byte objects.
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It appears that the performances of our metadata engine are acceptable, as we
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have a comfortable margin compared to Minio (Minio is between 3x and 4x times
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slower per batch). We also note that, past 200k objects, Minio batch
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completion time is constant as Garage's one remains linear: it could be
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interesting to know if Garage batch's completion time would cross Minio's one
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completion time is constant as Garage's one is still increasing in the observed range:
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it could be interesting to know if Garage batch's completion time would cross Minio's one
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for a very large number of objects. If we reason per object, both Minio and
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Garage performances remain very good: it takes respectively around 20ms and
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5ms to create an object. At 100 Mbps, if you upload a 10MB file, the
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@ -333,10 +333,11 @@ Next, we focus on Garage's data only to better see its specific behavior:
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![Showing the time to send 128 batches of 8192 objects for Garage only](1million.png)
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Two effects are now more visible: 1. batch completion time is linear with the
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Two effects are now more visible: 1. increasing batch completion time with the
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number of objects in the bucket and 2. measurements are dispersed, at least
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more than Minio. We discussed the first point previously but not the second
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one on measurement dispersion. This instability could be an issue as it could
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more than Minio. We don't know for sure if this increasing batch completion
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time is linear or logarithmic as we don't have enough datapoint; additinal
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measurements are needed. Concercning the observed instability, it could
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be a symptom of what we saw with some other experiments in this machine:
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sometimes it freezes under heavy I/O operations. Such freezes could lead to
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request timeouts and failures. If it occurs on our testing computer, it will
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@ -351,8 +352,8 @@ cluster at [deuxfleurs.fr](https://deuxfleurs) smoothly manages a bucket with
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116k objects. This bucket contains real data: it is used by our Matrix instance
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to store people's media files (profile pictures, shared pictures, videos,
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audios, documents...). Thanks to this benchmark, we have identified two points
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of vigilance: putting object duration seems linear with the number of existing
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objects in the cluster, and we have some volatility in our measured data that
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of vigilance: batch duration increases with the number of existing
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objects in the cluster in the observed range, and we have some volatility in our measured data that
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could be a symptom of our system freezing under the load. Despite these two
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points, we are confident that Garage could scale way above 1M+ objects, but it
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remains to be proved!
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