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Consistency (database systems)
Consistency (database systems)
Consistency (database systems)
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In database systems, consistency (or correctness) refers to the requirement that any given database transaction must change affected data only in allowed ways. Any data written to the database must be valid according to all defined rules, including constraints, cascades, triggers, and any combination thereof. This does not guarantee correctness of the transaction in all ways the application programmer might have wanted (that is the responsibility of application-level code) but merely that any programming errors cannot result in the violation of any defined database constraints.[1]

In a distributed system, referencing CAP theorem, consistency can also be understood as after a successful write, update or delete of a Record, any read request immediately receives the latest value of the Record.

As an ACID guarantee

[edit]

Consistency is one of the four guarantees that define ACID transactions; however, significant ambiguity exists about the nature of this guarantee. It is defined variously as:

  • The guarantee that database constraints are not violated, particularly once a transaction commits.[2][3][4][5][6]
  • The guarantee that any transactions started in the future necessarily see the effects of other transactions committed in the past.[7][8]

As these various definitions are not mutually exclusive, it is possible to design a system that guarantees "consistency" in every sense of the word, as most relational database management systems in common use today arguably do.

As a CAP trade-off

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The CAP theorem is based on three trade-offs, one of which is "atomic consistency" (shortened to "consistency" for the acronym), about which the authors note, "Discussing atomic consistency is somewhat different than talking about an ACID database, as database consistency refers to transactions, while atomic consistency refers only to a property of a single request/response operation sequence. And it has a different meaning than the Atomic in ACID, as it subsumes the database notions of both Atomic and Consistent."[7] In the CAP theorem, you can only have two of the following three properties: consistency, availability, or partition tolerance. Therefore, consistency may have to be traded off in some database systems.

See also

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References

[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

Consistency (database systems)

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In database systems, consistency refers to the property that ensures any transaction transforms the database from one valid state to another, preserving all defined constraints, rules, and specifications without violating invariants. This concept is fundamental to maintaining reliability and correctness, particularly in transactional environments where multiple operations must adhere to predefined protocols to avoid invalid states. As a core pillar of the ACID properties—alongside atomicity, isolation, and durability—consistency guarantees that committed transactions result in a coherent and valid database state, while uncommitted or failed ones leave the data unchanged. In centralized databases, this is typically enforced through mechanisms like constraint checking and locking to prevent anomalies during concurrent access. However, in distributed systems, consistency extends to ensuring uniformity across replicated nodes, where models range from strong consistency (e.g., linearizability, providing the illusion of a single atomic operation) to weaker forms like causal or eventual consistency, which allow temporary discrepancies for improved performance. The CAP theorem, formalized in distributed computing, highlights inherent trade-offs: in the event of network partitions, a system can guarantee at most two of consistency (all nodes see the same data simultaneously), availability (every request receives a response), and partition tolerance (the system continues operating despite communication failures). This has profoundly influenced modern database design, leading to tunable consistency models in NoSQL and NewSQL systems, such as MongoDB's strong consistency options or Cassandra's tunable eventual consistency, enabling developers to prioritize scalability or correctness based on application needs. Achieving optimal consistency often involves techniques like two-phase commit protocols, quorum reads/writes, or vector clocks to manage replication and synchronization.

Fundamentals

Definition

In database systems, consistency refers to the property that ensures any transaction transforms the database from one valid state to another valid state, thereby preserving all defined constraints, rules, invariants, and types. This means that if the database begins in a state where all specified conditions hold—such as unique primary keys, for foreign keys, and satisfaction of check constraints—the transaction's execution will result in a subsequent state that continues to satisfy these conditions, preventing invalid or semantically incorrect from persisting. A valid state in this context is one in which the entire database adheres to its predefined and business rules, including constraints like non-null values for required fields, domain-specific limits (e.g., age values between 0 and 120), and application-specific invariants (e.g., account balances never going negative in a banking ). Consistency thus enforces semantic correctness beyond mere , guaranteeing that transactions do not violate the logical structure or intended meaning of the data. For instance, a transfer between two accounts must deduct from one and add to the other without leaving the total balance inconsistent, even if the operation involves multiple steps. While related to atomicity, which ensures a transaction executes entirely or not at all (the "all-or-nothing" guarantee), consistency specifically addresses the validity of the resulting database state rather than just its completeness. Atomicity prevents partial updates that could leave the system in an indeterminate state, but it is consistency that verifies the post-transaction state aligns with defined rules; a transaction could be atomic yet inconsistent if it fails to respect constraints, though mechanisms like database triggers or checks typically prevent this. The concept of consistency, as a core transaction property, originated in the late 1970s through foundational work in research, with Jim Gray articulating it in his 1981 paper on transaction concepts, where he described transactions as preserving consistency constraints by transforming consistent states into new ones. The term gained prominence in the early 1980s as part of the framework, with Theo Härder and Andreas Reuter formalizing the acronym in their 1983 survey on transaction-oriented database recovery, building on Gray's earlier definitions to emphasize reliability in error-prone environments.

Importance

Consistency in database systems is fundamental to maintaining , as it ensures that the database remains in a valid state after every transaction, preventing anomalies such as duplicate records or violations of predefined rules. This preservation of valid states enables reliable querying and reporting, which are essential for operational and analytical processes. Without consistent , systems risk propagating errors that undermine the trustworthiness of outputs, potentially leading to flawed analyses or incorrect insights. In business contexts, the absence of consistency can result in severe consequences, particularly in domains requiring precise financial tracking. For instance, in banking systems, consistency mechanisms prevent overdrafts during fund transfers by ensuring that debit and credit operations occur atomically, maintaining account balances within legal limits and avoiding unauthorized negative states. Similarly, in e-commerce platforms, consistency safeguards against issues like oversold inventory by validating stock levels in real-time during purchase transactions, thereby preventing order fulfillment failures and customer dissatisfaction. These examples illustrate how consistency directly supports operational reliability and mitigates financial losses from erroneous transactions. While achieving often incurs performance trade-offs, such as increased latency from synchronization overhead, it remains indispensable for applications demanding immediate data accuracy. In systems, for example, consistent updates ensure that healthcare providers access the most current patient information, reducing risks of misdiagnosis or treatment errors that could endanger lives. This latency is a necessary cost for the heightened reliability in critical sectors, where even brief inconsistencies could have profound implications. The importance of consistency has evolved significantly since the 2000s with the proliferation of and distributed databases, driven by demands for in web-scale applications. Traditional relational systems prioritized , but emerging distributed architectures, such as those developed by major companies, intensified debates by introducing tunable consistency models to balance availability and performance. This shift highlighted the need for application-specific consistency choices, as seen in services where suffices for non-critical data like shopping carts, yet spurred innovations to reconcile with reliability needs.

Transactional Consistency

ACID Property

In database systems, the ACID properties form the foundational guarantees for reliable in relational databases. ACID is an acronym for Atomicity, Consistency, Isolation, and , first formally defined by Andreas Reuter and Theo Härder in their 1983 paper on transaction-oriented database recovery. Atomicity ensures that a transaction is treated as a single, indivisible unit, either fully completing or fully aborting without partial effects. Isolation prevents concurrent transactions from interfering with each other, maintaining the appearance of serial execution. Durability guarantees that once a transaction commits, its changes persist even in the event of system failures, typically through logging mechanisms. The Consistency property specifically requires that a transaction transitions the database from one valid state to another, preserving all defined constraints and rules. Upon successful completion (commit), only legal results are applied, ensuring the database remains consistent with its predefined invariants, such as , unique keys, and domain constraints; if a transaction would violate these, it must be rolled back. This enforcement occurs through database-specific mechanisms, including declarative constraints (e.g., CHECK clauses), triggers that automatically validate or adjust data during transactions, and stored procedures that encapsulate complex rule logic to maintain invariants. For instance, in a banking application, a transfer transaction debiting one account and crediting another must adhere to consistency by preventing any account balance from becoming negative if a CHECK constraint is defined on the balance column to enforce non-negative values. These guarantees, including consistency, are standardized in the ANSI/ISO SQL specifications, with foundational transaction support outlined in the standard (ISO/IEC 9075:1992), which defines transaction boundaries via BEGIN TRANSACTION, COMMIT, and ROLLBACK statements while implying consistency through constraint enforcement. Leading management systems (RDBMS) implement full compliance, such as , which has supported transactions since version 7.1 in 2001, and , which integrates properties natively in its multi-version concurrency control model.

Ensuring Consistency in Transactions

In systems, consistency during transaction execution is primarily ensured through defined isolation levels, which specify the degree to which concurrent transactions can interfere with one another. The ANSI standard outlines four isolation levels: Read Uncommitted, Read Committed, Repeatable Read, and Serializable. Read Uncommitted permits dirty reads, where a transaction can read uncommitted changes from another transaction, potentially leading to inconsistent views if the changes are rolled back. Read Committed prevents dirty reads by ensuring reads only access committed but allows non-repeatable reads, where the same query may yield different results within a transaction due to concurrent commits. Repeatable Read blocks both dirty reads and non-repeatable reads, guaranteeing consistent reads of the same item throughout the transaction, though it may still permit phantom reads from new inserts. Serializable offers the strongest consistency, preventing all such anomalies and ensuring the transaction executes as if it were the only one running, achieved through conflict serializability that equates concurrent executions to a serial order. Locking mechanisms form a core technique for enforcing isolation by controlling access to data items, typically using shared locks for reads and exclusive locks for writes. Shared locks (S-locks) allow multiple transactions to read the same data simultaneously but block any write attempts, preventing lost updates during concurrent reads. Exclusive locks (X-locks) grant a transaction sole access for reading or writing, blocking all other shared or exclusive locks on the item to maintain consistency. These locks are often managed via (2PL), where the growing phase acquires all necessary locks before any are released in the shrinking phase, ensuring serializability by avoiding cycles in the waits-for graph. To handle potential deadlocks—circular waits where transactions block each other indefinitely—databases employ detection algorithms that periodically scan the lock graph for cycles and resolve them by aborting and rolling back one involved transaction, minimizing throughput loss. Multi-version concurrency control (MVCC) complements locking by maintaining multiple versions of data items, allowing readers to access a consistent snapshot without blocking writers. In MVCC, each write operation creates a new version of the data, timestamped or certified upon commit, while retaining previous versions for ongoing readers. This prevents dirty reads by directing transactions to committed versions only, avoiding uncommitted changes. Lost updates are averted as writes append new versions rather than overwriting existing ones, with rules (such as timestamp ordering) ensuring the correct version is selected based on the transaction's start time, thus preserving the serializable order without lock contention on reads. Constraint enforcement further safeguards consistency by validating data integrity rules at transaction boundaries, particularly during the COMMIT phase. Primary keys ensure uniqueness and non-null values within a table, rejecting inserts or updates that duplicate existing keys to prevent inconsistent references. Foreign keys maintain by verifying that values in a child table match existing primary keys in a parent table, blocking operations that would create orphaned records. These checks, along with application-level validations, are typically deferred until COMMIT in supporting systems, allowing temporary violations during the transaction but ensuring the final state adheres to constraints for atomic consistency. For recovery from failures that could leave the database in an inconsistent state, (WAL) provides and capabilities. WAL requires all transaction modifications to be logged to stable storage before applying them to the database pages, enabling reconstruction post-crash. In the ARIES recovery algorithm, WAL supports three phases: to identify active transactions and dirty pages, redo to replay committed updates idempotently using log sequence numbers (LSNs), and to rollback uncommitted or loser transactions by reversing changes in reverse order, generating compensation log records to track rollbacks and prevent re-undoing. This ensures that even partial failures result in a consistent state, restoring properties without data loss.

Distributed Consistency

CAP Theorem

The CAP theorem states that in a distributed system, it is impossible to simultaneously guarantee consistency, , and partition tolerance. This fundamental limitation was first proposed as a by Eric Brewer during his keynote address at the 2000 Symposium on Principles of (PODC). It was formally proven and refined by Seth Gilbert and in 2002, establishing it as a theorem applicable to shared-data systems like . Within the CAP framework, consistency is defined such that every read operation from the system must return the most recent write or result in an , ensuring a linearizable view where operations appear to occur atomically in a . This contrasts with weaker consistency models but aligns with the need for up-to-date data in distributed environments. requires that every non-failing request to the system receives a response without , even if the response might not reflect the latest write. Partition tolerance, denoted as , acknowledges that network partitions—temporary failures in communication between nodes—are inevitable in distributed systems due to factors like hardware faults or congestion, and the system must continue operating despite them. The implies that distributed systems must prioritize two of these properties over the third, particularly since partition tolerance is non-negotiable in real-world networks where failures are unavoidable. For instance, systems aiming for both consistency and partition tolerance (CP) may sacrifice by blocking operations during partitions to prevent inconsistent reads. Conversely, choosing and partition tolerance (AP) allows responses during partitions but risks serving stale data, violating strict consistency. The proof of the relies on the asynchronous , where message delays are finite but unbounded, making consensus impossible without additional assumptions. Logically, during a partition that isolates a of nodes, maintaining consistency requires that the isolated nodes either halt responses to avoid propagating outdated writes or synchronize upon reconnection, which would block on the partitioned side until resolution. This trade-off demonstrates that no protocol can ensure all three properties hold indefinitely in the presence of partitions, forcing designers to make explicit choices based on application needs.

Trade-offs in Practice

In distributed database systems, practitioners must navigate the CAP theorem's implications by selecting architectures that align with application requirements, often favoring consistency and partition tolerance (CP) for scenarios demanding accurate data views, such as financial transactions, or availability and partition tolerance (AP) for high-throughput, fault-tolerant services like e-commerce recommendations. This choice influences system behavior during network partitions, where CP systems may halt operations to preserve , while AP systems continue serving potentially outdated information to maintain responsiveness. CP systems prioritize or , sacrificing when partitions prevent achievement; for instance, MongoDB's replica sets with write concern ensure writes only succeed if acknowledged by a of nodes, blocking operations during partitions that isolate the primary from sufficient members to maintain consistency. In contrast, AP systems like emphasize by allowing reads and writes to proceed on any available node, relying on tunable consistency levels (e.g., ONE for local or for balanced coordination) that permit stale reads during partitions but guarantee eventual convergence through anti-entropy mechanisms such as read repair. Similarly, defaults to for reads, enabling high by serving data from any replica, with available as an option at higher latency and cost, though it may fail requests during partitions if strict guarantees are enforced. Hybrid approaches attempt to mitigate trade-offs using advanced ; Google's Spanner, for example, employs atomic clocks and the TrueTime to bound clock uncertainty, enabling external consistency (a form of across transactions) while supporting through Paxos-based replication, though it approximates CP by occasionally stalling under high uncertainty to avoid inconsistencies. A seminal from Amazon's illustrates these priorities: for applications, was favored to ensure availability during peak loads and partitions—tolerating temporary data staleness, such as duplicate items, as users could resolve conflicts—whereas banking systems require CP-like guarantees to prevent overdrafts or erroneous balances, highlighting how workload semantics drive consistency choices.

Consistency Models

Strong Consistency

Strong consistency in database systems refers to models that ensure all reads reflect the most recent writes in a globally agreed-upon order, providing the illusion of a single, sequential execution across distributed nodes. The strongest such model is , which requires that operations appear to take effect instantaneously at some point between their invocation and response, respecting real-time ordering. This means concurrent operations are perceived as if they occurred sequentially, with no overlaps in their execution intervals, guaranteeing atomicity and immediate visibility of updates. A related and equally stringent model is strict serializability, which extends traditional serializability by imposing real-time constraints on the order of transactions. In serializability, transactions execute as if in some sequential order that preserves the database's consistency, but without regard to wall-clock time; strict serializability ensures this sequential order also respects the real-time precedence of transaction starts and finishes. This prevents anomalies such as write skew, where two transactions read overlapping data sets, validate constraints independently, and write updates that collectively violate those constraints despite appearing serial individually. For instance, in a banking application, two transactions might concurrently check account balances to approve transfers, leading to an if not strictly serialized. Implementing strong consistency typically involves consensus protocols to coordinate replicas and ensure agreement on operation order. Paxos, a foundational algorithm, achieves this by selecting a leader to propose values and using majority quorums to commit them, tolerating failures while maintaining a total order of updates. Raft simplifies Paxos by decomposing consensus into leader election, log replication, and safety mechanisms, enabling replicated state machines to execute commands in the same sequence across nodes. For read operations under strong consistency, quorum-based approaches require that the sum of write quorum (W) and read quorum (R) sizes exceeds the total number of replicas (N), ensuring reads capture the latest committed write (e.g., W + R > N). These mechanisms enforce linearizability or strict serializability but often at the cost of higher latency, as noted in CAP theorem analyses where strong consistency prioritizes consistency over availability during partitions. Strong consistency is essential in high-stakes domains like financial ledgers, where even brief inconsistencies could lead to monetary losses or regulatory violations. Systems such as Google's Spanner employ strict serializability via TrueTime APIs and two-phase commits to support global transactions in , ensuring all replicas reflect updates in real-time order without stale reads.

Weak Consistency

Weak consistency models in database systems prioritize and over immediate across replicas, allowing temporary inconsistencies that resolve over time in distributed environments. These models are particularly useful in scalable systems where strict ordering of all operations would introduce unacceptable latency or reduce throughput. By relaxing guarantees, weak consistency enables higher parallelism and , though it requires applications to handle potential conflicts explicitly. Eventual consistency is a foundational weak model where, if no new updates are made to an object, all replicas will eventually converge to the same state through and application of updates. This guarantee relies on total of updates and consistent ordering across replicas, ensuring liveness without immediate . The concept was introduced in system, which demonstrated its applicability in weakly connected environments by using operation logs and conflict detection to achieve convergence. Causal consistency extends eventual consistency by preserving the order of causally related operations while permitting non-causal operations to appear out of sequence. In this model, if one operation causes another (e.g., through a shared dependency), all processes observe them in the same relative order, but independent operations may be visible in different orders across replicas. This provides a balance between intuitive application semantics and , as it avoids global for unrelated events. The model was formally defined for systems, where vector timestamps track causal dependencies to enforce the ordering. Read-your-writes and monotonic reads offer per-session guarantees that mitigate common anomalies in weak models without requiring full system-wide consistency. Read-your-writes ensures that a session's subsequent reads reflect its own prior writes, preventing users from missing their updates. Monotonic reads guarantees that once a value is observed, future reads in the same session will not return an earlier version, maintaining a non-decreasing view of over time. These session-based properties enhance in replicated systems by associating guarantees with client contexts rather than global state. Practical implementations of weak consistency often employ vector clocks to detect and resolve conflicts arising from concurrent updates. For instance, , a distributed database, uses with vector clocks to track versions and detect siblings (conflicting replicas), allowing applications to resolve them via last-write-wins or custom merging strategies. Similarly, Voldemort, a key-value store developed by , supports tunable weak consistency through configurable read (R) and write (W) quorums, with vector clocks enabling client-side conflict resolution and asynchronous repair for eventual convergence. These systems illustrate how weak models facilitate in large-scale deployments, contrasting with by tolerating temporary divergences for improved .

References

  1. https://jimgray.azurewebsites.net/papers/theTransactionConcept.pdf
  2. https://arxiv.org/pdf/1902.03305.pdf
  3. https://users.ece.cmu.edu/~adrian/731-sp04/readings/GL-cap.pdf
  4. https://www.mongodb.com/docs/manual/core/read-isolation-consistency-recency/
  5. https://dl.acm.org/doi/10.1145/289.291
  6. https://www.mongodb.com/resources/basics/databases/acid-transactions
  7. https://www.allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf
  8. https://cacm.acm.org/research/replicated-data-consistency-explained-through-baseball/
  9. https://bmchealthservres.biomedcentral.com/articles/10.1186/s12913-023-10127-0
  10. https://www.postgresql.org/about/
  11. https://docs.oracle.com/en/database/oracle/tuxedo-mainframe-adapter/22c/tmaag/acid-properties.html
  12. https://dl.acm.org/doi/pdf/10.1145/568271.223785
  13. https://www.cmi.ac.in/~madhavan/courses/databases10/knapp-Computing-Surveys.pdf
  14. https://www.cs.cmu.edu/~15712/papers/bernstein83.pdf
  15. https://learn.microsoft.com/en-us/sql/relational-databases/tables/primary-and-foreign-key-constraints?view=sql-server-ver17
  16. https://web.stanford.edu/class/cs345d-01/rl/aries.pdf
  17. https://dl.acm.org/doi/10.1145/564585.564601
  18. https://www.usenix.org/system/files/conference/osdi12/osdi12-final-16.pdf
  19. https://dl.acm.org/doi/pdf/10.1145/1323293.1294281
  20. https://cassandra.apache.org/doc/latest/cassandra/architecture/guarantees.html
  21. https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadConsistency.html
  22. https://dl.acm.org/doi/10.1145/78969.78972
  23. https://cs.brown.edu/~mph/HerlihyW90/p463-herlihy.pdf
  24. https://arxiv.org/pdf/1208.4179
  25. https://lamport.azurewebsites.net/pubs/paxos-simple.pdf
  26. https://raft.github.io/raft.pdf
  27. https://research.google.com/archive/spanner-osdi2012.pdf
  28. https://www.cs.utexas.edu/~lorenzo/corsi/cs380d/papers/p172-terry.pdf
  29. https://www.researchgate.net/publication/3561300_Session_guarantees_for_weakly_consistent_replicated_data
  30. https://link.springer.com/article/10.1007/BF01784241
  31. https://docs.riak.com/riak/kv/latest/learn/concepts/eventual-consistency/index.html
  32. https://docs.riak.com/riak/kv/latest/developing/usage/conflict-resolution/index.html
  33. https://cs.stanford.edu/people/rsumbaly/files/voldemort.pdf
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