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Postgres vs Fauna: Terminology and features
Evan Weaver|Jan 20th, 2021|
Postgres and Fauna are both relational, operational databases. They serve similar use cases in modern applications, and as you will see below, they have many similarities, especially in their positive attributes. At the same time, they differ significantly in architecture, query language support, and deployment model.
PostgreSQL, also known as Postgres, is one of the most popular open source databases in the world. Derived from the Ingres project, it has been developed by an open source community for over two decades. It is a traditional relational database management system designed for interactive transactions over low latency networks. Many cloud infrastructure vendors now offer Postgres as a service, with varying degrees of support for the full range of Postgres features.
Postgres’ architecture originated in the pre-internet era, when operational databases were used for predictable business reporting workloads from trusted applications within the same building on a LAN. Various managed infrastructure providers have made proprietary changes to Postgres’ code base to better fit the cloud world, but the fundamentals of the architecture remain. In comparison, Fauna’s architecture originates in the serverless era, and is designed for scalable access from globally distributed applications over the public internet.
Postgres and Fauna are both relational databases, supporting serializable transactions, database normalization, foreign keys, indexes, constraints, stored procedures, and other typical relational features. Postgres has some features Fauna lacks, like geo-indexing, whereas Fauna has some features Postgres lacks, like temporality, query streaming, and multi-tenancy. Postgres offers SQL as a query language, while Fauna supports GraphQL and FQL.
|Row or record||Document||An individual record in the database.|
|Table||Collection||A container for documents.|
|Primary||Region||Fauna has no primary or secondary concept, so all regions can serve both reads and writes.|
|Secondary, replica, standby||Region||Fauna has no primary or secondary concept, so all regions can serve both reads and writes.|
|Replication||Replication||Fauna’s replication is semi-synchronous and does not require any operator management.|
|Sharding||n/a||Fauna does not require the operator to manage sharding or partitioning in any way.|
|Primary key||Ref||The unique identifier of a document.|
|Foreign key||Ref||A pointer from one document to another.|
|Index, materialized view||Index||Fauna merges the concepts of indexes and views. Indexes must be explicitly referenced, similar to the concept of hinting.|
|Transaction||Transaction||Both Postgres and Fauna support ACID transactions.|
|Schema, database||Database||Both Postgres and Fauna have a concept of logical databases that include schema (also known as DDL) records describing the collections, indexes, and security properties of the included records.|
|Stored procedure, user-defined function||Function||Fauna supports user-defined functions written in FQL.|
Postgres is based on the tabular SQL data model, while Fauna is based on schemaless documents, to better fit modern application development patterns. (As a historical note, initially Postgres did not support SQL, but rather supported the QUEL language, which is similar in semantics but incompatible in syntax. Postgres’ popularity began to grow in the 90s after the addition of SQL support. This has some similarities to how Fauna’s popularity grew with the addition of GraphQL support.)
Postgres’ query feature set is very rich, with many custom extensions to the SQL standard, including full-text search, the JSONB document data type and other custom types, pgSQL stored procedures, a unique administrative security model, and various analytical capabilities. However, this means it is not directly compatible with any other SQL database, which limits portability. In practice, database workloads are never truly portable across DBMS implementations because of capability, administrative, and performance differences.
Fauna also has a rich feature set, offering object and array types, document streaming, temporality, attribute-based access control (ABAC), multi-tenancy, integration with third-party identity and authorization providers, and a standard library similar to that of a general-purpose programming language. However, Fauna also is not directly compatible with any other database, unless usage is restricted to the GraphQL API.
Postgres’ fundamental query paradigm is that of making declarative, analytical queries against a large dataset. However, for operational, short-request workloads, these queries are restricted to single or small groups of records via WHERE clauses; this, along with the tabular data model, creates an impedance mismatch that ORMs are designed to solve.
ORMs introduce significant application complexity without removing the need to understand the SQL model and its performance characteristics, and undermine the value of core Postgres features like stored procedures and views that perform complex work outside of the application layer.
In comparison, Fauna’s query paradigm is one of customizing an operational data API via procedural programming. Fauna does not require ORMs or other middleware, instead offering a query model that directly matches modern application development paradigms.
You can compare SQL and FQL in detail here.
Postgres and Fauna implement indexes very differently. In Postgres, an index is a materialization of an access pattern that the query planner can implicitly use to accelerate existing queries. In Fauna, an index is more like a relational view: a materialization that the application can query explicitly.
One challenge with index design in Postgres and other relational databases is the implicit nature of their use at runtime. Frequently a developer believes a query is efficiently indexed when it is not. Various tools exist to try to identify unused indexes and unoptimized queries, and even change the storage strategy the index uses, but there is always some degree of trial and error in proper index design.
In Fauna, it is transparent when an index is queried and when it is not, because the query must refer to it explicitly and rely on its contents directly. This means that existing queries cannot automatically be improved simply by adding an index. However, it also means that there are no discontinuities in the performance profile of the query as the dataset or query changes, simply because the planner started using a different strategy. In relational databases, index hinting is one technique to help mitigate this problem. You can think of Fauna indexes as an always-hinted system.
Both Postgres and Fauna indexes can enforce unique constraints, and both Postgres and Fauna allow indexing into deeply nested JSON objects and multi-value arrays. Postgres supports built-in geographic and full-text indexing, which Fauna currently lacks, although there are some workarounds via FQL. However, unlike Postgres, Fauna indexes can include multiple collections and create constraints across them, as well as filter and transform indexed data in more complex ways. Fauna can also query historical data, even in indexes.
Postgres and Fauna schema design is similar. Both systems encourage relational data modeling and normalization. Both systems support foreign keys and unique constraints. Both offer transactional guarantees that preserve data integrity across collections and tables. Compared to non-relational systems, data modeling in Postgres and Fauna is easy, intuitive, and safe.
One difference is that Postgres schemas are declared and enforced at runtime for each table, whereas Fauna is schemaless. Instead, schemas are implied by the shape of the documents. Fauna supports more flexible schema evolution for this reason.
To enforce specific schemas within Fauna, you can use application-side checks, or use functions (stored procedures) for writes, which lets the database run custom logic before transactions are committed. Declarative schemas are on the Fauna roadmap.
Another difference is that Fauna also has graph database roots. For example, in Postgres and other relational databases, one-to-many relationships would be constructed via join tables. In Fauna, a join collection works well, but you can also choose to directly refer from one document to another via references (foreign keys) within the documents, and index whatever query patterns you want to pre-materialize. The index functions as a system-managed join table, simplifying the data model.
In general, Fauna schema design can be considered an evolution of Postgres’ relational model, with better support for modern document and object-oriented programming practices and standards like GraphQL.
Postgres and Fauna are both transactional databases. Postgres supports interactive sessions transactions over TCP/IP. The application opens a persistent connection to the database that buffers transactional state, and can incrementally build up the transaction over the wire, interleaving database reads, application-side computation, and database writes.
The persistent connection model has some disadvantages in the cloud era. It expects to interact with long-lived application instances that are physically co-located with the database, not globally-distributed web servers, serverless functions, or embedded clients. Connection overhead is high, leading to cold-start problems. The series of interactions between the client and the server require low-latency network links to be performant. And because the server must maintain persistent resources for each connection, connection exhaustion is possible, leading to complex connection pooling solutions that interleave requests, affecting consistency and availability.
Fauna, on the other hand, does not need persistent transaction state. Fauna transactions are stateless, atomic, pure functions over the state of the dataset. Applications using Fauna build up a transaction locally via the Fauna drivers. This transaction is then serialized to JSON and sent to the database as a single HTTPS request, minimizing latency.
This also means, however, that the application cannot do local computation during the course of the transaction—instead it must rely on Fauna’s rich standard library of functions for server-side computation in the transaction itself. In practice, this is rarely a point of friction.
This model eliminates the need for connection management. It dramatically reduces latency, especially for geographically diverse applications, increases throughput, and simplifies security and network operations.
On the CAP theorem continuum, Postgres and Fauna are both CP systems. If forced to choose, they favor consistency over high availability. Both databases offer a strictly serializable consistency model for read/write transactions, and are ACID-compliant. Nevertheless there are some very significant differences that make this comparison not as straightforward as it may seem.
Postgres defaults to the read-committed isolation level, which is much weaker than serializable. Enabling serializable isolation has some performance costs. Additionally, Postgres only offers transactional isolation for transactions that interact with the primary node.
Most applications perform all read-write transactions on the Postgres primary, but route read-only transactions to secondaries to allow for read scale-out and lower latency. These read-only transactions are eventually consistent. The results can be stale or even invalid under common scenarios without any notice to the application. Read-your-own-writes is not guaranteed. Even when Postgres is configured for semi-synchronous replication, this situation does not improve. Postgres will block writes on secondary commits, but will not isolate secondary reads.
Enabling multi-primary writes in Postgres degrades the isolation model even further, as constraints can no longer be enforced. Avoiding these problems requires using read replicas as hot standbys only, leading to the obvious scalability bottlenecks on the primary node. Postgres’ roots as a single location, vertically scaled RDBMS are clear when the isolation model is combined with replication.
In contrast, Fauna defaults to strict serializability, the highest possible isolation level. This guarantee is preserved for all read-write transactions, regardless of which region they are performed in. This reduces latency and increases durability. Unlike Postgres, there are no circumstances in which Fauna can lose an acknowledged commit.
Fauna read-only transactions default to snapshot isolation, which is similar to Postgres’ read-committed default for all transactions, and are always served from the closest region to the application without further coordination. This minimizes latency. However, Fauna also uses some special techniques that upgrade read-only snapshot isolation to strictly serializable in all normally observable scenarios, without increasing latency.
Thus, Fauna’s consistency model is stronger than Postgres’ model for horizontally scalable and/or highly available deployments.
Postgres has a complex security model that has grown in complexity over time. For basic access, it offers the ability to inherit security properties and accounts from the underlying operating system, limiting access to datasets and administrative resources. It allows for creation of administrative accounts directly within the database itself. It offers connections over TCP/IP, with optional encryption via SSL, as well as connections via Unix domain sockets.
Once access is achieved, various administrative rights can be granted for table access and schema changes. Additionally, row-level security is possible by creating policy predicates that are checked before performing read or write operations.
Postgres security is designed for a small number of administrators of the database itself. It is not designed to manage security at the application user level. This task is completely delegated to the application itself. The developer must decorate every query with appropriate access restrictions, and consider the implications of indirect access and other leak vectors in the dataset. This can lead to many security faults, such as the venerable SQL injection attack, where malformed user data can truncate a query before its security clauses and defeat them.
Fauna, on the other hand, was designed with a web-native security model. It is secure by default and does not assume that any access is trusted. All applications must connect to the database over HTTPS with an access key that defines the administrative privileges that application has.
Additionally, Fauna’s security model additionally assumes user-level security is the common case. Users can be authenticated and identified with third-party providers or with Fauna directly, and be issued a key that limits their access, which is controlled by attribute-based access control policies, which are similar to Postgres’ predicates, but more flexible and composable.
Fauna also offers a hierarchical tenancy model that lets datasets within the same database be completely isolated from each other, for example, different customers of a SaaS product. Such a data model in Postgres requires the addition of a tenancy column to every table and a filter clause to every query, introducing complexity and risk.
Additionally, query parsing in Fauna is type safe, and injection attacks are not possible.
Replication and high availability
Postgres’ replication is based on a traditional primary and secondary concept. All write transactions must be directed to the primary node. After they commit on the primary node, the write effects are asynchronously replicated to any secondary nodes. (Some previous versions of Postgres required statement-based replication based on triggers and middleware.)
The Postgres replication architecture introduces several issues in a modern cloud environment. The reliance on a primary node creates a single point of failure, reducing availability. It also increases latency because it can not be globally distributed. It is a scalability bottleneck, requiring vertical (per table) partitioning as a workaround, which undermines transactional guarantees. Serializability and read-your-own-writes guarantees are not met when reading from the secondary nodes. Finally, if the primary node fails, it is possible to lose committed writes, as well as suffer a lengthy failover process during which writes are non-performant.
Fauna’s replication is based on a Calvin-inspired, global, replicated transaction log. All transactions are journaled to the log before they are committed. Commits to the replica regions are semi-synchronous; the application does not receive an acknowledgement until durability, serializability, and read-your-own-write guarantees are met. This applies regardless of which region the application accesses, and even if the application switches regions between requests.
There is no concept of a primary or secondary region in Fauna, nor is there a user-visible concept of a node. The Fauna API manages failures of the underlying infrastructure transparently for both reads and writes, and latency is bound by the physical distance to the nearest Fauna region.
Postgres scalability is bound on two axes: the capacity of the primary node, which principally impacts write transitions (and the read dependencies of those writes), and the number and capacity of the secondary nodes, which can scale reads. Thus, writes are vertically scalable, and reads are both horizontally and vertically scalable. In order to scale in most Postgres clusters, manual provisioning is required, or at minimum, configuration of auto-scaling parameters.
Postgres is not, at heart, an elastic system. Scaling up and down must be a predictive process, because it takes time to shutdown and restart processes on new hardware, VMs, or in new containers, rehydrate caches, and copy data from secondary nodes. Cloud Postgres systems that are serverless or auto-scaled typically rely on making many micro-provisioning decisions on an hourly or minutely basis to simulate an API-style elasticity. This impacts latency, throughput, and potentially availability, depending on configuration. It also leads to wasted, idle resources.
Fauna’s writes scale with the transaction log, which is both partitioned and replicated, and thus horizontally and vertically scalable. Fauna’s reads scale with the number and size of the replica regions, which may be heterogeneous given the balance of workloads within Fauna overall. However, these scaling decisions are not visible to the developer at all.
Fundamentally, Fauna is a multi-tenant API, not a provisioned, managed resource. All workloads within Fauna share a large pool of underlying hardware that is both multi-region and multi-cloud. The Fauna scheduler within the database kernel tracks resource consumption and implements something akin to cooperative multithreading to isolate queries from each other.
Thus there is no concept of scaling a database up and down within Fauna. All workloads potentially have access to the resources of the entire cluster at any moment in time. The Fauna operational team scales the shared Fauna clusters behind the scenes as overall usage grows. This model minimizes idle resources and waste, and helps guarantee predictable performance regardless of the individual query patterns within a database.
Postgres is open source, and relies on operator deployment onto provisioned hardware. Many cloud vendors offer managed versions of Postgres that orchestrate this deployment via a dashboard or administrative API.
This model exposes quite a bit of complexity, but also choice, to the operator. The operator controls which version of Postgres is deployed, coupling operational and performance improvements, bug fixes, and new features together. The operator must also select the underlying hardware for the database, which can dramatically change the performance not only of different types of queries, but also the running costs of the system.
Postgres has a substantial library of first-party and third-party extensions that may or may not be available or configurable. Tuning parameters that are available for things like buffer caches, connection defaults, background tasks behavior (such as the vacuum task that garbage collects deleted records) all affect performance and configuration. Additionally, Postgres failover models vary and require understanding of the host environment’s capabilities and the database’s configuration policies in order to manage hardware faults appropriately from the application.
In contrast, Fauna abstracts these decisions away. The Fauna operations team manages the cluster overall, continually upgrading the underlying software and hardware to improve performance across the board for all Fauna databases. Fauna databases have no configurability at the operational level beyond region selection, and performance is bound by query patterns and dataset sizes, not by tunable parameters.
Open source has many benefits, but in this case, since Fauna is proprietary, one of its downsides is avoided. There is no feature fragmentation: every Fauna database has access to the same capabilities, including those in the local development edition (which is a copy of the actual cloud software, and not a mock). There is no upgrade cycle or version selection for the operator; compatibility is maintained through API versioning instead.
Jepsen, and its related tool Elle, is a verification suite for the consistency properties of transactional systems, under normal operation, and under fault conditions. Both Fauna and Postgres have been evaluated under Jepsen at various times in the past.
In both evaluations, the Jepsen team found implementation bugs that affected the consistency properties, which have since been resolved by respective maintainers of the databases. In many Jepsen analyses, the underlying system cannot possibly deliver its claimed capabilities due to fundamental issues in the architectures. Importantly, the Jepsen team found that both Fauna’s and Postgres’ architectures achieve their goals in both theory and practice.
One important thing to note is that a Jepsen analysis only checks for the properties that the system under testing claims to offer—it is not testing against an absolute standard. Since Postgres does not offer multi-primary write consistency, 100% durability on failover, or serializable reads from secondary nodes, these properties were not checked.
Fauna does offer these properties, and they were verified as such. Thus, Jepsen ultimately demonstrated that although Fauna is newer than Postgres, it does indeed offer an overall higher level of consistency.
Fauna and Postgres share many similar goals and characteristics. Although Postgres is older than Fauna, they are both tested and hardened transactional databases with very strong durability, consistency, and availability properties. Their differences are most apparent in two ways: their query languages and their operational posture.
Postgres is the archetypal RDBMS, like DB2 or Oracle. It is designed for high-availability, mainframe class hardware, with co-located, trusted applications making SQL queries on a low-latency LAN.
Fauna, on the other hand, is two or even three generations advanced. Designed recently rather than in the 80s and 90s, it reflects modern development and deployment patterns: globally distributed applications that use document and graph APIs to securely access data over the public internet.
If you like Postgres, especially its transactional properties and battle-hardened reliability, but want to minimize your operational burden and maximize your productivity in the modern cloud and serverless era, you may like Fauna even more. Give it a try!
If you enjoyed our blog, and want to work on systems and challenges related to globally distributed systems, serverless databases, and GraphQL, Fauna is hiring!
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