By Zhihong Yao, Software Engineer, Google Cloud Storage
Here at Google Cloud, we’re proud of the fact that, all of our listing operations are consistent across Google Cloud Storage. They’re consistent across all Cloud Storage bucket locations, including regional and multi-regional buckets. They’re consistent whether you’re listing buckets in a project or listing objects within a bucket. If you create a Cloud Storage bucket or object, and then you follow that up with a request to fetch a list of resources, your resource will be in that response.
Why is this important? Strong list consistency is a big deal when you run data and analytics workloads. Here’s an explanation from Johannes Fabian Rußek, Technical Product Owner at Spotify on why strongly consistent listing operations are so important to his business:
When you do not have consistent listings, there is a possibility of missing files. You cannot rely on the consistency of the data being read as you develop your products. Even worse, inconsistent listings lead to unforeseen issues. For example, our processing tooling will succeed reading partial data and may potentially produce seemingly valid outputs. Problems like these have a tendency to quickly propagate throughout the dependency tree.
When that happens, in the best-case we notice the failure and recompute all datasets produced within the dependency tree. In the worst-case, the failure goes unnoticed and we create invalid reports and statistics. Considering the large amount of data pipelines we run, even with a low probability of that happening, a lack of list-consistency in cloud storage offerings was a major blocker for data-processing at Spotify.
Not all cloud storage services provide list-after-write consistency, which can cause challenges for some common use cases. Typically, when a user uploads an object to a cloud storage bucket, an unpredictable and unbounded amount of time passes before that object shows up in that bucket’s list of contents. This is a very weak consistency model called “eventual consistency.” In practice, if a user uploads a new object and then tries to find it from a browser on another computer, they might not see the object that they just uploaded. Similar issues impact workloads distributed across multiple compute nodes. By offering strong list consistency across all Google Cloud Storage objects, you avoid having to wrangle with these sorts of problems. Again, here’s Spotify’s Johannes Fabian Rußek:
We considered multiple workarounds, such as using a global consistency cache based on NFS, porting Netflix’s s3mper as well as persisting listings in a manifest file stored alongside the data. All of the considered solutions were suboptimal as they either introduced a single point of failure or required us to put significant resources into developing our own solution and adjusting our tooling. Strong list consistency in Cloud Storage means we can continue using our existing data-processing stack without modifications and without worrying that data may be corrupted.
List consistency on Cloud Storage is an essential feature for data processing at Spotify. We use a Hadoop-based data processing stack built on top of the Hadoop Distributed File System, which means we rely on its filesystem-like guarantees. Consistency is critical to running our business, and its absence creates many challenges.
Spanner: the secret to Cloud Storage strong list consistency
Up until last year, Cloud Storage stored information about its buckets and objects (metadata) in a group of technologies powered by an internal Google technology called “Megastore.” Megastore enabled Cloud Storage to provide important features like read-after-write consistency quickly and at very high volumes. But as is typical for object storage, Cloud Storage only provided eventual consistency for list-after-write operations.
Last year we migrated all of Cloud Storage metadata to Spanner, Google’s globally distributed and strongly consistent relational database. Spanner’s specialty is scaling horizontally while providing strong consistency guarantees and high availability. The same technology is available to Google Cloud customers today as Cloud Spanner.
Migrating to Spanner afforded Cloud Storage some key new features, including strong listing consistency. Other beneficiaries of strong listing consistency are MapReduce or Hadoop tasks, in which many workers need to produce separate work that will later be collected by some other processor. With strong listing consistency, workers can separately upload their results to a bucket, confident that a collecting job will always be able to collate all of the results with no exceptions. This is another example of how strong or external consistency makes application development more efficient.
Cloud Storage now provides strong consistency for the following operations in all types of buckets in all regions:
- Read-after-write consistency: Reading an object after writing it has completed, for both new objects and overwrites of existing objects
- Read-after-update consistency: Reading an object’s metadata after updating its metadata
- Read-after-delete consistency: Reading an object will fail with a 404 immediately after it has been deleted
- List-after-write consistency: Fetching a list of buckets and objects will always reflect any changes that have previously completed
- Granting additional permissions for access to resources: For example, when you grant a new user permission to read an object, that user can immediately read the object
- Some operations or permissions changes provide bounded consistency. Such as read operations on public cached objects, designed to achieve top cache performance. Details can be found here.
We’re excited by the new functionality that hosting Cloud Storage metadata on Spanner brings to the table. You can learn more about Cloud Storage’s consistency model in our documentation: https://cloud.google.com/storage/docs/consistency.
Feed Source: Google Cloud Platform Blog
Article Source: How Google Cloud Storage offers strongly consistent object listing thanks to Spanner