TAO: The power of the graph

 Here is the article. 

I like to read the article again. It is tough for me to learn the basics from the article. I will figure out the most important concepts in the article first. 

My notes: 

Requirement: the data set must be retrieved and rendered on the fly in a few hundred milliseconds

Facebook puts an extremely demanding workload on its data backend. Every time any one of over a billion active users visits Facebook through a desktop browser or on a mobile device, they are presented with hundreds of pieces of information from the social graph. Users see News Feed stories; comments, likes, and shares for those stories; photos and check-ins from their friends -- the list goes on. The high degree of output customization, combined with a high update rate of a typical user’s News Feed, makes it impossible to generate the views presented to users ahead of time. Thus, the data set must be retrieved and rendered on the fly in a few hundred milliseconds.

two factors to consider:

1. the high degree of output customization
2. a high update rate of a typical user's News Feed
3. It is impossible to generate the views presented to users ahead of time. - The argument

Data set is not easily partitionable

photos of celebrities, request rates spike significantly

multiple this by the millions of times per second - 

read-dominated workload 

This challenge is made more difficult because the data set is not easily partitionable, and by the tendency of some items, such as photos of celebrities, to have request rates that can spike significantly. Multiply this by the millions of times per second this kind of highly customized data set must be delivered to users, and you have a constantly changing, read-dominated workload that is incredibly challenging to serve efficiently.

Memcache and MySQL

Facebook has always realized that even the best relational database technology available is a poor match for this challenge unless it is supplemented by a large distributed cache that offloads the persistent store. Memcache has played that role since Mark Zuckerberg installed it on Facebook’s Apache web servers back in 2005. As efficient as MySQL is at managing data on disk, the assumptions built into the InnoDB buffer pool algorithms don’t match the request pattern of serving the social graph. The spatial locality on ordered data sets that a block cache attempts to exploit is not common in Facebook workloads. Instead, what we call creation time locality dominates the workload -- a data item is likely to be accessed if it has been recently created. Another source of mismatch between our workload and the design assumptions of a block cache is the fact that a relatively large percentage of requests are for relations that do not exist -- e.g., “Does this user like that story?” is false for most of the stories in a user’s News Feed. Given the overall lack of spatial locality, pulling several kilobytes of data into a block cache to answer such queries just pollutes the cache and contributes to the lower overall hit rate in the block cache of a persistent store.

What is the following statement: 

the lower overall hit rate in the block cache of a persistent store.

persistent store -> block cache -> lower overall hit rate 

The use of memcache vastly improved the memory efficiency of caching the social graph and allowed us to scale in a cost-effective way. However, the code that product engineers had to write for storing and retrieving their data became quite complex. Even though memcache has “cache” in its name, it’s really a general-purpose networked in-memory data store with a key-value data model. It will not automatically fill itself on a cache miss or maintain cache consistency. Product engineers had to work with two data stores and very different data models: a large cluster of MySQL servers for storing data persistently in relational tables, and an equally large collection of memcache servers for storing and serving flat key-value pairs derived (some indirectly) from the results of SQL queries. Even with most of the common chores encapsulated in a data access library, using the memcache-MySQL combination efficiently as a data store required quite a bit of knowledge of system internals on the part of product engineers. Inevitably, some made mistakes that led to bugs, user-visible inconsistencies, and site performance issues. In addition, changing table schemas as products evolved required coordination between engineers and MySQL cluster operators. This slowed down the change-debug-release cycle and didn’t fit well with Facebook's “move fast” development philosophy.

memcache 

a general-purpose networked in-memory data store with a key-value data model

Product engineers had to work with two data stores and very different data models: a large cluster of MySQL servers for storing data persistently in relational tables, and an equally large collection of memcache servers for storing and serving flat key-value pairs derived (some indirectly) from the results of SQL queries.

How I quickly go over the content in my own words? 8/9/2020 3:53 PM

Product engineers 

two data stores - data models are different 

a large cluster of MySQL servers for storing data persistently in relational tables

an equally large collection of memcache servers - storing and serving flat key-value pairs derived (some indirectly) from the results of SQL queries

Objects and associations

In 2007, a few Facebook engineers set out to define new data storage abstractions that would fit the needs of all but the most demanding features of the site while hiding most of the complexity of the underlying distributed data store from product engineers. The Objects and Associations API that they created was based on the graph data model and was initially implemented in PHP and ran on Facebook's web servers. It represented data items as nodes (objects), and relationships between them as edges (associations). The API was an immediate success, with several high-profile features, such as likes, pages, and events implemented entirely on objects and associations, with no direct memcache or MySQL calls.

As adoption of the new API grew, several limitations of the client-side implementation became apparent. First, small incremental updates to a list of edges required invalidation of the entire item that stored the list in cache, reducing hit rate. Second, requests operating on a list of edges had to always transfer the entire list from memcache servers over to the web servers, even if the final result contained only a few edges or was empty. This wasted network bandwidth and CPU cycles. Third, cache consistency was difficult to maintain. Finally, avoiding thundering herds in a purely client-side implementation required a form of distributed coordination that was not available for memcache-backed data at the time.

All those problems could be solved directly by writing a custom distributed service designed around objects and associations. In early 2009, a team of Facebook infrastructure engineers started to work on TAO (“The Associations and Objects”). TAO has now been in production for several years. It runs on a large collection of geographically distributed server clusters. TAO serves thousands of data types and handles over a billion read requests and millions of write requests every second. Before  we take a look at its design, let’s quickly go over the graph data model and the API that TAO implements.

Design -> objects and associations, API design:

product engineers - with no direct memcache or MySQL calls.

The Objects and Associations API that they created was based on the graph data model and was initially implemented in PHP and ran on Facebook's web servers. It represented data items as nodes (objects), and relationships between them as edges (associations). The API was an immediate success, with several high-profile features, such as likes, pages, and events implemented entirely on objects and associations, with no direct memcache or MySQL calls.

likes, pages, and events implemented entirely on objects and associations, with no direct memcache or MySQL calls. 

TAO data model and API

This simple example shows a subgraph of objects and associations that is created in TAO after Alice checks in at the Golden Gate Bridge and tags Bob there, while Cathy comments on the check-in and David likes it. Every data item, such as a user, check-in, or comment, is represented by a typed object containing a dictionary of named fields. Relationships between objects, such as “liked by" or “friend of," are represented by typed edges (associations) grouped in association lists by their origin. Multiple associations may connect the same pair of objects as long as the types of all those associations are distinct. Together objects and associations form a labeled directed multigraph.

Normal description of task:

Alice checks in at the Golden Gate Bridge and tags Bob there, while Cathy comments on the check-in and David likes it.

Data items:

a user, check-in, or comment - a typed object containing a dictionary of named fields

Relationships between objects, liked by or friend of, typed edges (associations) grouped in association lists by their origin. 

multigraph - objects and associations form a labeled directed multigraph

The set of operations on objects is of the fairly common create / set-fields / get / delete variety. All objects of a given type have the same set of fields. New fields can be registered for an object type at any time and existing fields can be marked deprecated by editing that type’s schema. In most cases product engineers can change the schemas of their types without any operational work.

Associations are created and deleted as individual edges. If the association type has an inverse type defined, an inverse edge is created automatically. The API helps the data store exploit the creation-time locality of workload by requiring every association to have a special time attribute that is commonly used to represent the creation time of association. TAO uses the association time value to optimize the working set in cache and to improve hit rate.

The simplicity of TAO API helps product engineers find an optimal division of labor between application servers, data store servers, and the network connecting them.