![]() ![]() " will split the transfer in multiple buckets while the source is scanned… The main limitation is it does not handle remote source or target directory, they must be locally accessible (local disk, nfs/cifs/other mountpoint)."This particular wrapper is simple to install, consisting of a single Python file. Parallel rsync can be set up using a wrapper like this one: This is where parallel rsync comes in: as opposed to standard rsync, parallel rsync isn't limited to copying a single chunk of data at a time and can, instead, copy several pieces side-by-side-hence its name. Parallel RsyncWe've found that in situations like the one described above, we can significantly speed up a very slow copying process by running several rsync processes at a time, each with a subset of the data. (We were suspicious that io latency was the primary culprit.) We checked cpu/network/io consumption and none seemed even close to performing at capacity. We first tried standard rsync to handle the recreation, but the time to copy was far too long. Additionally, if the volume io has high latency-such as when cold Amazon EBS volumes are involved-the throughput can suffer, as rsync will only copy one chunk of data at a time.Īs engineer Alejandro Martinez summarized for me, our team recently had to re-create a replica for a MySQL server with more than three terabytes of data. In such a case, the process can take hours. Rsync can still be slow in certain situations, however-especially when there's a high volume of data that needs to be copied. The advantage of rsync is that instead of copying data blindly, it compares the source and destination directories, so that only the difference between the two is sent through the network (or between volumes). Rsync is a tool for copying files between volumes in the same or separate servers. ![]() In this post, we'll take a quick look at rsync ("remote sync") and parallel rysnc-a way to increase the efficiency and speed of traditional rsync-both of which can be effective and handy at various times. ![]()
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