Jepsen analyzed Amazon RDS for PostgreSQL 17.4 using various workloads, including single-object, multi-object, and bank transfers, under different failure modes like network partitions and forced failovers. They found several serializability violations across all workloads, often involving read skew and lost updates. While RDS typically provides strong consistency within a single Availability Zone (AZ), cross-AZ and read replicas exhibited weaker consistency guarantees, leading to anomalies. These inconsistencies were observed even with the "strong" read consistency setting enabled. Despite these issues, RDS generally recovered from failures and maintained availability. The report concludes that users requiring strict serializability should employ external mechanisms like explicit locking or causal consistency tracking.
Kyle Kingsbury, operating under the Jepsen project, conducted a series of fault injection tests on Amazon RDS for PostgreSQL version 17.4, focusing on its consistency guarantees under various failure scenarios. The primary goal was to evaluate the database's adherence to its advertised isolation levels: Read Committed, Repeatable Read, Serializable, and Read Committed with Read-Only Transactions. The testing leveraged Jepsen's Clojure framework, specifically targeting a three-node RDS cluster deployed in Amazon's us-east-2 region.
The investigation explored the impact of network partitions, both full and partial, alongside planned and unplanned failovers. Unplanned failovers were simulated by forcibly terminating the primary node. Network partitions involved manipulating security groups to selectively disrupt communication between nodes. The test scenarios systematically varied the timing and duration of these disruptions to thoroughly probe the system's behavior under stress.
The results revealed several critical inconsistencies. Under Read Committed isolation, the tests observed both read skew anomalies and lost updates, violating the expected guarantees of this isolation level. Read skew manifests as a transaction reading different versions of data within the same transaction due to concurrent modifications. Lost updates occur when concurrent transactions overwrite each other's changes, effectively losing data. These anomalies can lead to data corruption and application errors.
Repeatable Read, while generally behaving as expected, exhibited a subtle vulnerability related to the interaction between long-running transactions and schema changes. Specifically, if a long-running transaction spanned a schema alteration, such as adding or dropping a column, subsequent transactions within the same session could encounter errors. This edge case necessitates careful management of long transactions within applications to prevent unexpected failures.
Serializable isolation, the strongest level offered, successfully prevented all classic anomalies, upholding its intended strict consistency guarantees. However, the tests highlighted the performance cost associated with this level of isolation, as expected.
The Read Committed with Read-Only Transactions setting exhibited the same weaknesses as standard Read Committed isolation, demonstrating its susceptibility to read skew and lost updates. This indicates that simply marking transactions as read-only does not enhance isolation guarantees.
Overall, the Jepsen analysis revealed that Amazon RDS for PostgreSQL 17.4 does not fully adhere to its claimed isolation levels for Read Committed and Read Committed with Read-Only Transactions, potentially leading to data inconsistencies in real-world applications. While Serializable isolation performed as expected, its performance implications warrant consideration. The findings regarding Repeatable Read and schema changes expose a nuanced edge case requiring careful handling. The analysis recommends developers thoroughly understand these limitations and adopt appropriate mitigation strategies, including potentially employing stronger isolation levels or application-level consistency checks, depending on the specific requirements of their workloads.
Summary of Comments ( 118 )
https://news.ycombinator.com/item?id=43833195
The Hacker News comments discuss the Jepsen analysis of Amazon RDS for PostgreSQL 17.4, mostly focusing on the surprising finding of stale reads even with read-after-write consistency selected. Several commenters express concern about the implications for applications relying on strong consistency. Some speculate about potential causes, including caching layers or complexities within RDS's implementation of logical replication. Others point out the trade-offs between consistency and availability, and the importance of carefully choosing the right consistency model for a given application. A few users share their own experiences with RDS consistency issues, while others question the practicality of Jepsen tests in real-world scenarios. The overall sentiment leans towards cautiousness regarding relying on RDS for strong consistency guarantees, emphasizing the need for thorough testing and potentially implementing application-level workarounds.
The Hacker News post titled "Jepsen: Amazon RDS for PostgreSQL 17.4" has several comments discussing the Jepsen analysis of Amazon RDS. Many commenters express a general appreciation for the Jepsen analyses and their contribution to understanding distributed systems' complexities.
Several commenters focus on the nuanced nature of the trade-offs between consistency and availability, particularly within the context of managed cloud services. They acknowledge that perfect consistency in all scenarios is often impractical, and the choices made by Amazon RDS, while leading to some anomalies under specific failure conditions, are potentially justifiable given the performance and availability requirements of many real-world applications. One commenter points out that the observed anomalies, while technically violations of strict serializability, might not necessarily translate into significant real-world problems for many users. They suggest that understanding the specific types of anomalies and their potential impact on an application is crucial.
Another thread of discussion revolves around the difference between the theoretical guarantees provided by database systems and the practical realities of operating them, especially in complex cloud environments. Commenters highlight the challenges in translating theoretical models to distributed settings and the potential for unexpected behaviors due to factors like network partitions and clock skew. The importance of thorough testing, as exemplified by Jepsen, is emphasized in this context.
Some comments delve into the specific technical details of the anomalies reported in the Jepsen analysis. They discuss the implications of using logical replication in PostgreSQL and how it might contribute to the observed inconsistencies. The role of transaction IDs and the challenges of maintaining global ordering in a distributed setting are also mentioned.
There's also some discussion about the responsibility of cloud providers like Amazon in clearly communicating the limitations and potential trade-offs of their managed services. While acknowledging the inherent complexities, commenters suggest that more transparency about the potential for consistency anomalies could help users make more informed decisions. One commenter even raises the point that the observed behaviors might not be considered bugs by Amazon, but rather inherent consequences of design choices optimized for specific use cases.
Finally, some commenters express skepticism about the practical relevance of Jepsen analyses, arguing that they often focus on highly contrived failure scenarios that are unlikely to occur in real-world deployments. However, counter-arguments suggest that while these scenarios might be rare, they can still have significant consequences when they do occur, and understanding the system's behavior under such conditions is crucial for building robust applications. Furthermore, the Jepsen tests can uncover subtle bugs and design flaws that might not be readily apparent in typical testing scenarios.