AWS Database Blog

Amazon Aurora Under the Hood: Reducing Costs Using Quorum Sets

Anurag Gupta runs a number of AWS database services, including Amazon Aurora, which he helped design. In this under the hood series, Anurag discusses the design considerations and technology underpinning Aurora.

This post is the third in a four-part series discussing how Amazon Aurora uses quorums. I hope the discussion is useful to you as you design your own distributed systems. In this post, we discuss how to manage costs in a quorum system.

Amazon Aurora under the hood series

The basic problem we’re tackling is that Aurora uses a quorum of six copies spread across three Availability Zones (AZs), using four out of six copies to write and three out of six copies for read/repair. In the first post of this series, I discussed why six is the minimum number of copies necessary. In my second post, I discussed how we can avoid the performance penalties of quorums for both writes and reads. But it’s still a lot of copies of data, and that carries costs. The low price for storage in Amazon Aurora might make us think that there’s something unusual going on. There is.

To understand what we do, you have to go back to the basic definition of a quorum. People generally talk about quorums as a set of like elements where the write set represents a majority of elements, and the read and write sets overlap. Although this is correct, it’s a simplification. The basic requirement is only that the read and write sets are subsets of the entire quorum membership set, for any legal write subset, at least one member is also contained within any legal read subset, and that each write subset overlaps with prior write subsets. That seems like the same thing, but it’s not.

The difference is that there is no requirement that quorum members be the same as each other. We can construct quorum sets that mix and match quorum subsets that have different latency, cost, or durability characteristics. We can then use the rules of Boolean logic to create more sophisticated read and write rules across the subsets to meet quorum membership requirements for the full quorum. Let’s examine how we do this in Aurora to reduce costs.

Mixing full and tail segments of data
In Aurora, a database volume is made up of 10 GB segments of data. These segments are replicated as a protection group, with six copies spread across three AZs. But the six copies are not all the same. Half of the copies are full segments, which contain both data pages and log records for that 10 GB portion of the volume. The other half are tail segments, which contain only log records. Each AZ contains one full segment and one tail segment.

Most databases have far more data block storage than redo log storage. Using a mix of full and tail segments takes the physical storage requirements of Aurora from six times the size of the database to a little bit more than three. For a system that is designed to tolerate “AZ+1” failure cases, that’s the minimum replication factor you can have.

The use of a mix of full segments and tail segments changes how we have to construct our read and write sets. You can use the rules of Boolean logic to ensure overlap across subsets and do it accurately even for arbitrarily complex distribution of members. In our case, our write quorum is four out of six of any segment OR three out of three of full segments. Our read quorum is then three out of six of any segment AND one out of three of full segments. You can see from the preceding that we have an overlap on all segments in the quorums as well as an overlap on our full segments. By doing this, we can write log records to the four out of six segments that we did previously. At least one of these is a full segment and generates a data page. We read data from full segments, using the optimization described in the last post to avoid quorum reads, and instead reading from the one we know has the data we need.

We use the read quorum as a way to rebuild failed segments and repair impaired quorums. We also use it to rebuild our local state if we have to restart the database write master node. If one of our tail segments fails, that’s easy. We just repair it from any one of the three other copies we know was written, just as we would in a simple quorum model.

If one of our full segments fails, it’s a bit more complicated. The one that failed could have been the copy that we wrote to as part of our write. But in that case, we know that we have another full segment, even if it hasn’t seen the most recent write. We also have enough copies of the redo log record that we can rebuild a full segment to be up to date. We also gossip between the segments of a quorum to ensure that any missing writes are quickly filled in. This reduces the probability we need to rebuild a full segment without adding a performance burden to our write path.

Controlling costs with quorum sets of unlike members
Using quorum sets of unlike members is a good way to contain costs. There are many options available. You might have quorums that combine the local disk for low latency and remote disks for durability/availability. You might have quorums that combine SSDs for performance and throughput and HDDs to improve durability through low-cost replicas. You might have quorums that combine data across AWS Regions to improve disaster recovery. There are a lot of moving parts that you need to get right, but the payoffs can be significant. For Aurora, the quorum set model described earlier lets us achieve storage prices comparable to low-cost alternatives, while providing high durability, availability, and performance.

So far in this series, we’ve discussed how to size quorums, how to avoid the penalty of read and write amplification, and, with this post, how to control costs. In the next post, we talk about how to manage quorum membership in a large-scale distributed system in the face of failures without expensive coordination.

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Read Next: Amazon Aurora Under the Hood: Quorum Membership