replace dummy buffer with nullReader{} instead,
to avoid large memory allocations in memory
constrainted environments. allows running
obd tests in such environments.
Currently, listing directories on HDFS incurs a per-entry remote Stat() call
penalty, the cost of which can really blow up on directories with many
entries (+1,000) especially when considered in addition to peripheral
calls (such as validation) and the fact that minio is an intermediary to the
client (whereas other clients listed below can query HDFS directly).
Because listing directories this way is expensive, the Golang HDFS library
provides the [`Client.Open()`] function which creates a [`FileReader`] that is
able to batch multiple calls together through the [`Readdir()`] function.
This is substantially more efficient for very large directories.
In one case we were witnessing about +20 seconds to list a directory with 1,500
entries, admittedly large, but the Java hdfs ls utility as well as the HDFS
library sample ls utility were much faster.
Hadoop HDFS DFS (4.02s):
λ ~/code/minio → use-readdir
» time hdfs dfs -ls /directory/with/1500/entries/
…
hdfs dfs -ls 5.81s user 0.49s system 156% cpu 4.020 total
Golang HDFS library (0.47s):
λ ~/code/hdfs → master
» time ./hdfs ls -lh /directory/with/1500/entries/
…
./hdfs ls -lh 0.13s user 0.14s system 56% cpu 0.478 total
mc and minio **without** optimization (16.96s):
λ ~/code/minio → master
» time mc ls myhdfs/directory/with/1500/entries/
…
./mc ls 0.22s user 0.29s system 3% cpu 16.968 total
mc and minio **with** optimization (0.40s):
λ ~/code/minio → use-readdir
» time mc ls myhdfs/directory/with/1500/entries/
…
./mc ls 0.13s user 0.28s system 102% cpu 0.403 total
[`Client.Open()`]: https://godoc.org/github.com/colinmarc/hdfs#Client.Open
[`FileReader`]: https://godoc.org/github.com/colinmarc/hdfs#FileReader
[`Readdir()`]: https://godoc.org/github.com/colinmarc/hdfs#FileReader.Readdir
If there are many listeners to bucket notifications or to the trace
subsystem, healing fails to work properly since it suspends itself when
the number of concurrent connections is above a certain threshold.
These connections are also continuous and not costly (*no disk access*),
it is okay to just ignore them in waitForLowHTTPReq().
this is to detect situations of corruption disk
format etc errors quickly and keep the disk online
in such scenarios for requests to fail appropriately.
String x might contain trimming spaces. And it needs to be trimmed. For
example, in csv files, there might be trimming spaces in a field that
ought to meet a query condition that contains the value without
trimming spaces. This applies to both intCast and floatCast functions.
Fixes two different types of problems
- continuation of the problem seen in FS #9992
as not fixed for erasure coded deployments,
reproduced this issue with spark and its fixed now
- another issue was leaking walk go-routines which
would lead to high memory usage and crash the system
this is simply because all the walks which were purged
at the top limit had leaking end walkers which would
consume memory endlessly.
closes#9966closes#10088
not all claims need to be present for
the JWT claim, let the policies not
exist and only apply which are present
when generating the credentials
once credentials are generated then
those policies should exist, otherwise
the request will fail.
- copyObject in-place decryption failed
due to incorrect verification of headers
- do not decode ETag when object is encrypted
with SSE-C, so that pre-conditions don't fail
prematurely.
This PR adds support for healing older
content i.e from 2yrs, 1yr. Also handles
other situations where our config was
not encrypted yet.
This PR also ensures that our Listing
is consistent and quorum friendly,
such that we don't list partial objects
Adam Brown