content/blog/2022-perf/index.md

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