Managing Your Cloud-Based Data Storage with Rclone | by Chaim Rand | Nov, 2023


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How to optimize data transfer across multiple object storage systems

Chaim Rand

Towards Data Science

Photo by Tom Podmore on Unsplash

As companies become more and more dependent on cloud-based storage solutions, it is imperative that they have the appropriate tools and techniques for effective management of their big data. In previous posts (e.g., here and here) we have explored several different methods for retrieving data from cloud storage and demonstrated their effectiveness at different types of tasks. We found that the most optimal tool can vary based on the specific task at hand (e.g., file format, size of data files, data access pattern) and the metrics that we wish to optimize (e.g., latency, speed, or cost). In this post, we explore yet another popular tool for cloud-based storage management — sometimes referred to as “the Swiss army knife of cloud storage” — the rclone command-line utility. Supporting more than 70 storage service providers, rclone supports similar functionality to vendor-specific storage management applications such as AWS CLI (for Amazon S3) and gsutil (for Google Storage). But does it perform well enough to constitute a viable alternative? Are there situations in which rclone would be the tool of choice? In the following sections we will demonstrate rclone’s usage, assess its performance, and highlight its value in a particular use-case — transferring data across different object storage systems.


This post is not, by any means, intended to replace the official rclone documentation. Nor is it intended to be an endorsement of the use of rclone or any of the other tools we should mention. The best choice for your cloud-based data management will greatly depend on the details of your project and should be made following thorough, use-case specific testing. Please be sure to re-evaluate the statements we make against the most up to date tools available at the time you are reading this.

The following command line uses rclone sync in order to sync the contents of a cloud-based object-storage path with a local directory. This example demonstrates the use of the Amazon S3 storage service but could just as easily have used a different cloud storage service.

rclone sync -P \
--transfers 4 \
--multi-thread-streams 4 \
S3store:my-bucket/my_files ./my_files

The rclone command has dozens of flags for programming its behavior. The -P flag outputs the progress of the data transfer including the transfer rate and overall time. In the command above we included two (of the many) controls that can impact rclone’s runtime performance: The transfers flag determines the maximum number of files to download concurrently and multi-thread-streams determines the maximum number of threads to use to transfer a single file. Here we have left both at their default values (4).

Rclone’s functionality relies on the appropriate definition of the rclone configuration file. Below we demonstrate the definition of the remote S3store object storage location used in the command line above.

type = s3
provider = AWS
access_key_id = <id>
secret_access_key = <key>
region = us-east-1

Now that we have seen rclone in action, the question that arises is whether it provides any value over the other cloud storage management tools that are out there such as the popular AWS CLI. In the next two sections we will evaluate the performance of rclone compared to some of its alternatives in two scenarios that we have explored in detail in our previous posts: 1) downloading a 2 GB file and 2) downloading hundreds of 1 MB files.

Use Case 1: Downloading a Large File

The command line below uses the AWS CLI to download a 2 GB file from Amazon S3. This is just one of the many of methods we evaluated in a previous post. We use the linux time command to measure the performance.

time aws s3 cp s3://my-bucket/2GB.bin .

The reported download time amounted to roughly 26 seconds (i.e., ~79 MB/s). Keep in mind that this value was calculated on our own local PC and can vary greatly from one runtime environment to another. The equivalent rclone copy command appears below:

rclone sync -P S3store:my-bucket/2GB.bin .

In our setup, we found the rclone download time to be more than two times slower than the standard AWS CLI. It is highly likely that this could be improved significantly through appropriate tuning of the rclone control flags.

Use Case 2: Downloading a Large Number of Small Files

In this use case we evaluate the runtime performance of downloading 800 relatively small files of size 1 MB each. In a previous blog post we discussed this use case in the context of streaming data samples to a deep-learning training workload and demonstrated the superior performance of s5cmd beast mode. In beast mode we create a file with a list of object-file operations which s5cmd performs in using multiple parallel workers (256 by default). The s5cmd beast mode option is demonstrated below:

time s5cmd --run cmds.txt

The cmds.txt file contains a list of 800 lines of the form:

cp s3://my-bucket/small_files/<i>.jpg <local_path>/<i>.jpg

The s5cmd command took an average time of 9.3 seconds (averaged over ten trials).

Rclone supports a functionality similar to s5cmd’s beast mode with the files-from command line option. Below we run rclone copy on our 800 files with the transfers value set to 256 to match the default concurrency settings of s5cmd.

rclone -P --transfers 256 --files-from files.txt S3store:my-bucket /my-local

The files.txt file contains 800 lines of the form:


The rclone copy of our 800 files took an average of 8.5 seconds, slightly less than s5cmd (averaged over ten trials).

We acknowledge that the results demonstrated thus far may not be enough to convince you to prefer rclone over your existing tools. In the next section we will describe a use case that highlights one of the potential advantages of rclone.

These days it is not uncommon for development teams to maintain their data in more than one object store. The motivation behind this could be the need to protect against the possibility of a storage failure or the decision to use data-processing offerings from multiple cloud service providers. For example, your solution for AI development might rely on training your models in the AWS using data in Amazon S3 and running data analytics in Microsoft Azure using the same data stored in Azure Storage. Additionally, you may want to maintain a copy of your data in a local storage infrastructure such as FlashBlade, Cloudian, or VAST. These circumstances require the ability to transfer and synchronize your data between multiple object stores in a secure, reliable, and timely fashion.

Some cloud service providers offer dedicated services for such purposes. However, these do not always address the precise needs of your project or may not enable you the level of control you desire. For example, Google Storage Transfer excels at speedy migration of all of the data within a specified storage folder, but does not (as of the time of this writing) support transferring a specific subset of files from within it.

Another option we could consider would be to apply our existing data management towards this purpose. The problem with this is that tools such as AWS CLI and s5cmd do not (as of the time of this writing) support specifying different access settings and security-credentials for the source and target storage systems. Thus, migrating data between storage locations requires transferring it to an intermediate (temporary) location. In the command below we combine the use of s5cmd and AWS CLI to copy a file from Amazon S3 to Google Storage via system memory and using Linux piping:

s5cmd cat s3://my-bucket/file \
| aws s3 cp --endpoint-url
--profile gcp - s3://gs-bucket/file

While this is a legitimate, albeit clumsy way of transferring a single file, in practice, we may need the ability to transfer many millions of files. To support this, we would need to add an additional layer for spawning and managing multiple parallel workers/processors. Things could get ugly pretty quickly.

Data Transfer with Rclone

Contrary to tools like AWS CLI and s5cmd, rclone enables us to specify different access settings for the source and target. In the following rclone config file we add settings for Google Cloud Storage access:

type = s3
provider = AWS
access_key_id = <id>
secret_access_key = <key>

type = google cloud storage
provider = GCS
access_key_id = <id>
secret_access_key = <key>
endpoint =

Transferring a single file between storage systems has the same format as copying it to a local directory:

rclone copy -P S3store:my-bucket/file GSstore:gs-bucket/file

However, the real power of rclone comes from combining this feature with the files-from option described above. Rather than having to orchestrate a custom solution for parallelizing the data migration, we can transfer a long list of files using a single command:

rclone copy -P --transfers 256 --files-from files.txt \
S3store:my-bucket/file GSstore:gs-bucket/file

In practice, we can further accelerate the data migration by parsing the list of object files into smaller lists (e.g., with 10,000 files each) and running each list on a separate compute resource. While the precise impact of this kind of solution will vary from project to project, it can provide a significant boost to the speed and efficiency of your development.

In this post we have explored cloud-based storage management using rclone and demonstrated its application to the challenge of maintaining and synchronizing data across multiple storage systems. There are undoubtedly many alternative solutions for data transfer. But there is no questioning the convenience and elegance of the rclone-based method.

This is just one of many posts that we have written on the topic of maximizing the efficiency of cloud-based storage solutions. Be sure to check out some of our other posts on this important topic.

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