A globally distributed application is one where the services and data for the application are partitioned and replicated across multiple regions over the globe. Popular distributed applications that everyone is familiar with are Facebook, Amazon.com, Gmail, Twitter, Instagram. However more and more enterprise applications are finding the need to become distributed because their user base is increasingly distributed around the globe. But not every company has the expertise of a Facebook or Amazon or Google. When going distributed, it is not enough to just spin up instances of your service on AWS or Google cloud on various regions. There are issues related to data that must be addressed for the application to work correctly. While consumer centric social media applications can tolerate some correctness issues or lags in data, the same might not be true for enterprise applications. This blog discusses the data and database issues related to a globally distributed application. Lastly, we discuss 2 research papers that been around since early part of this decade, but whose relevance is increasing in recent times.
Let us look at some common correctness issues that crop up with distributed data.
Example 1 : A distributed on line store with servers in San Francisco, New York and Paris.
Each server has 2 tables products and inventory with the following data.
Products:(product)
widget1
widget2
Inventory: (product, count):
widget1,6
widget2,1
Customer Jose connects to server in San Francisco and buys widget2 at time t1. At time t2, Customer Pierre connects to a server in Paris and also buys widget2. Assume t2 > t1 but t2-t1 is small.
Expected Behavior : Jose successfully completes transaction and gets the product. Since inventory of widget2 is now zero, Pierre’s transaction is aborted.
Actual Behavior (in an eventually consistent system): Both transactions complete. But only one of the customers gets the product. The other customer is later sent an apologetic email that widget2 is out of stock.
Example 2: A distributed document sharing system with servers in New York, London, Tokyo
Operation1: In London, User X creates a new empty document marked private.
Operation2. User X makes update 1 to document.
Operation3: User X deletes update 1.
Operation4: User X makes update 2.
Operation5: User X changes the document from private to public.
Due to network issues, only operations 1,2, 5 reach Tokyo. 3 and 4 do not.
In Tokyo, User Y tries to read the shared document.
Expected behavior: The document status is private and Y cannot read the document.
Actual behavior: Y is able to read the document but an incorrect version. The document has update1 which is deleted and is missing update2 which needs to be there.
The problems above are known as consistency issues. Different clients are seeing different views of the data. What is the correct view ?
Consistency here refers to C in the CAP theorem, not the C in ACID. Here Consistency means every thread in a concurrent application correctly reads the most recent write at that point in time.
How do you fix the above issues ? In a single node database, Example1 can be fixed by locking the row in the inventory table during update and Example2 is not even an issue because all the data is in one node. But in a distributed application data might be split across shards and shards replicated for high availability. User of the system might connect to any shard/server and read/write data. With NoSql databases, the application has to handle any in consistencies.
In traditional RDBMSs , database developers are given a knob called isolation level to control what concurrent threads can read. In this old blog I explain what isolation levels are. The safest isolation level is the SERIALIZABLE where the database behaves as if the transactions were executing in a serial order with no overlap, even though in reality they are executing concurrently. Most developers use the default isolation level which is generally READ_COMMITTED OR READ_REPEATABLE. In reality, these isolation levels are poorly documented and implemented differently by different vendors. The result is that in highly concurrent applications, there are consistency bugs even in traditional single node RDBMs. In a distributed database with data spread across shards and replicated for read scalability, the problem is compounded further. Most NoSql vendors punt the problem by claiming eventual consistency, meaning if there are no writes for a while, eventually all reads on all nodes will read the last write.
Consistency is often confused with isolation, which describes how the database behave under concurrent execution of the transactions. At the safest isolation level, the database behaves as if the transactions were executing in serial order, even though in reality they are executing concurrently. At the safest consistency level, every thread in a concurrent application correctly reads the most recent write. But most database documentations are not clear on how to achieve this in an application.
The problems in examples 1 and 2 would not occur if those applications/databases had the notion of a global transaction order with respect to real time. In example 1, Pierre’s transaction at t2 should see the inventory as 0 because a transaction at t1 <t2 set it to zero. In example 2, Y should only be able to read upto operation2 . It should not be able to read operation5 without operations 3,4 which occured before 5.
In database literature, the term for this requirement is called “Strict Serializability” or sometimes “external consistency”. Since this technical definitions can be confusing, it is often referred to as strong consistency.
2 research papers that have been around for a while provide answers on how this problems might be fixed. The papers are the Spanner paper and the Calvin paper.
Their approach is solving the problem can summarized as follows:
1. timestamp transactions with something that reflect their occurrence in real time
2. Order transactions based on timestamp
3. Commit transactions in the above order.
But the details of how they do it are significantly different. Let us look at how they do it.
Spanner paper from Google
Spanner is database built at Google and the paper describes the motivation and design of Spanner. Spanners approach involves
1. The use of atomic clocks and GPS to synchronize clocks across hosts in different regions and the true time API to give accurate time across nodes, regions or continent.
2. For a read/write transaction, spanner calls the true time API to get a timestamp. To address overlaps between transactions that are close to each other, the timestamp is assigned after locks are acquired and before they are released.
3. The commit order equals timestamp order.
4. Read for particular timestamp is sent to any shard/replica that has the data at that timestamp.
5. Read without timestamp (latest read) are serviced by assigning a timestamp.
6. Writes that cross multiple shards use two phase commit.
And of course,
7. It can scale horizontally to 1000s of nodes by sharding.
8. Each shard is replicated.
And most importantly,
9. Even though, it is a key value store, it provide SQL support to make it easy for application programmers.
CockroachDb and Yugabyte are 2 commercial databases based on spanner.
The Calvin paper addresses the above problem using distributed consensus protocols like Raft or Paxos.
1. Every transaction has to first go through distributed consensus and secure a spot in a linear replication log.
2. One can view the index in the log as the timestamp.
3. The committed entries in the replication log are then executed in the exact same serial order by every node in the distributed database.
4. Since the transaction log is replicated to every shard, it does not need or use two phase commit. In a transaction involving multiple shards, if a shard dies before committing a particular transaction, then on restart it just has to execute the uncommitted transaction from it replication log.
5. No dependency on wall clocks or time API.
6. No two phase commit.
7. No mention of SQL support.
FaunaDb is an example of a database based on Calvin.
This class of databases that offer horizontal scalability on a global scale without sacrificing consistency is also called NewSql.
In summary, if you are a building a globally distributed application that needs strong consistency, doing it on your own with SQL or NoSql database can be non trivial. Consistency is hard enough in a single node database. But on a distributed database, consistency bugs are harder to troubleshoot and even harder to fix. You might want to consider one of the NewSql databases to make life easier. Review the Spanner and Calvin papers to understand the architectural choices that are available. This will help you pick a database that is right for you. Spanner and Calvin papers have been around for almost a decade. But they have become more relevant now as real databases based on them become more popular. Most importantly understand what is consistency is and apply it, for lack of which can cause severe correctness bugs in your application.
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