What is a distributed transactional database?
A distributed transactional database is a database system that spreads data across multiple nodes or locations while still supporting ACID-compliant transactions that maintain data integrity. In such systems, transactions can involve data on different nodes yet maintain the same guarantees of atomicity, consistency, isolation, and durability that a single-node database does. In effect, a distributed transaction integrates operations on multiple databases, including remote databases across multiple servers in a distributed environment, into one unit, while a local transaction involves only one database. This combination lets applications scale while maintaining strong data integrity. Historically, maintaining a consistent and persistent database state across distributed nodes has been challenging.
Architecture and data distribution
Distributed transactional databases typically use a shared-nothing architecture where data is partitioned (sharded) across many nodes, and each node runs the same database software. Horizontal scaling through partitioning helps the system handle growing data and workload by adding nodes.
It’s important to avoid hotspots by distributing data and workloads evenly so that no one node becomes a bottleneck. Each partition (or shard) holds a subset of the data, and a coordination mechanism maps transactions to the correct nodes responsible for the relevant data. This shared-nothing design means each node operates independently on its partition of the data, relying on network communication for any cross-node operations. The cluster’s distributed data must be carefully partitioned to balance load, and related records are often kept together on the same shard to perform local transactions when possible, reducing multi-node distributed transactions.
Distributed databases also replicate data across multiple nodes to make them more fault tolerant and available. Each data partition often has a primary copy and one or more replicas on different nodes or racks. If one node fails, a replica takes over serving that data, keeping the database up.
Many distributed databases replicate transaction data synchronously: Each write is propagated to replicas at commit time to keep them up-to-date. This means once a transaction is committed on the primary node, the transactional data is durably stored on several nodes, so no data is lost even if a server crashes. Some systems use quorum consensus or other algorithms to coordinate replicas. The tradeoff is that writing data to multiple nodes can add latency, but it makes the system more durable and ensures all users see the same data even after failures.
In practice, running on multiple servers requires robust failure handling: Today’s systems use automatic failover by promoting a replica to primary if a node crashes and rebalancing data to restore redundancy. This way, the distributed data storage remains consistent and available, at the cost of some extra latency due to replication overhead.
ACID transactions and strong consistency
A defining characteristic of transactional databases is adherence to the ACID properties of atomicity, consistency, isolation, and durability. In a distributed context, ACID is extended across nodes:
All parts of a distributed transaction either commit or none do (atomicity)
The system moves from one valid state to another according to defined rules (consistency)
Concurrent transactions do not interfere with each other’s intermediate data (isolation)
Once committed, the results survive failures (durability)
Enforcing these properties in a distributed setup is more complex than on one node because multiple machines and network links are involved in each transaction. Each node must support local ACID properties, and the system as a whole must support these properties globally.
Maintaining strong data consistency means every user or application sees the same view of data across the distributed system at any given time. Distributed transactional databases are usually designed as “CP” systems under the CAP theorem: They prioritize consistency and partition tolerance over absolute availability. Consistency prevents anomalies like reading stale or partial data, which is important for transactional workloads that require up-to-date information, such as a financial transfer that must debit and credit accounts in sync. Many earlier NoSQL database models sacrificed ACID guarantees to maximize availability and scalability, resulting in eventual consistency models.
In contrast, today’s distributed transactional systems aim for strict serializability, the strongest isolation level, so transactions appear to execute in one globally ordered sequence. This often requires concurrency control and commit protocols.
Concurrency control and distributed commit
In any transactional database, a concurrency control mechanism provides isolation (the "I" in ACID) so that simultaneous transactions do not corrupt each other’s data. In distributed databases, the challenge is to preserve a global ordering or serializability even though transactions execute on different nodes.
In practice, many systems use techniques such as two-phase locking (2PL) across the cluster or multi-version concurrency control to make sure they perform transactions in the right order. For example, if all participating nodes use strict 2PL for concurrency control, the overall system runs all the transactions in the right order across the cluster. Some of today’s designs use timestamp ordering or synchronized clocks for the same effect without explicit locking by having each transaction carry a globally unique timestamp (essentially a global transaction ID) and committing in timestamp order.
For atomicity across multiple nodes, distributed databases rely on a special commit protocol. The most common is the two-phase commit (2PC) protocol. In 2PC, a central transaction coordinator, often called a transaction manager or distributed transaction coordinator, does this in two steps: It asks all involved nodes to prepare. Each node effectively marks its part of the transaction, its transaction branch, as ready to commit, and to report if it can commit successfully.
If and only if all participants vote “yes” to prepare, the coordinator sends a commit instruction to finalize the transaction on every node.
This two-phase process means either every participant makes the changes permanent or, if any one cannot proceed, all participants abort and roll back their part, maintaining atomicity across the cluster. In other words, all the transaction branches must agree to commit; if any branch fails or votes to abort, the coordinator cancels the entire transaction and initiates rollbacks on every participant, so no partial changes occur.
Variants such as the XA standard in relational databases use a different approach for distributed transactions. During the prepare phase, each node typically records the pending transaction in a log and may hold locks, so that even if a crash occurs, it recovers and either commits or aborts consistently after reboot. Two-phase commit is a foundational transaction solution for distributed systems. It underpins the standard X/Open XA transactions that many enterprise environments use.
While 2PC guarantees a consistent outcome, it introduces additional overhead and latency. All parties must coordinate and wait for each other, so a slow or unresponsive participant delays all the nodes. Moreover, the system must pause between the prepare and commit phases, which adds network round-trips. If the coordinator or any node fails, other nodes might be left waiting and holding locks until the issue is resolved. In such a case, the transaction may be left in an unresolved state, often called an “in-doubt” transaction status, requiring recovery or manual intervention to decide whether to commit or roll back once the systems come back online. This means distributed transactions can be slower and can even block in some failures, unlike local transactions, which either commit or fail quickly. Techniques such as timeouts, automatic failure detection, and consensus algorithms (Paxos/Raft) help mitigate these problems because the database recovers from partial failures. Nevertheless, the complexity of coordinating multiple nodes is a fundamental reason why distributed ACID transactions are more challenging than single-node transactions.
Consistency vs. availability (CAP trade-offs)
The CAP theorem states that any distributed system cannot simultaneously guarantee consistency, 100% availability, and partition tolerance. At best, it performs only two of the three. Distributed transactional databases lean towards CP (consistency + partition tolerance): They view data consistently and operate correctly across network partitions, but they may sacrifice some availability in rare failure cases.
For example, if a network partition occurs between two data centers, a strongly consistent database might halt transactions in one partition, making that partition temporarily unavailable for writes, rather than allow conflicting updates that could lead to an inconsistent state. In contrast, some eventually consistent systems choose AP (availability + partition tolerance), meaning they accept writes on both sides of a partition at the cost of immediate consistency, reconciling differences later.
The classic two-phase commit approach exemplifies the CP choice: It blocks progress if not all nodes agree, which means that during a network issue, the system might become unavailable to preserve consistency. This trade-off is acceptable and often necessary in scenarios where data integrity is paramount. For instance, one would prefer to pause a banking service during a network glitch rather than allow money to “disappear” or be duplicated due to inconsistent updates.
No distributed system can avoid escaping CAP. Even the most advanced distributed databases cannot guarantee both absolute consistency and uninterrupted availability in the face of network partitions. What today’s databases do is optimize for as much availability as possible while being consistent, using techniques such as fast failure detection and leader elections. They improve uptime during common failures, but when a choice must be made, transactional systems will usually support consistency over accepting divergent writes. Indeed, today’s distributed SQL databases explicitly follow the CP model: They use consensus replication such as Paxos or Raft to coordinate writes and will pause updates, or degrade to read-only mode, during a partition or primary failure rather than compromise data consistency.
Understanding this CAP trade-off is important when designing or choosing a distributed database. Systems that require strong correctness, such as financial ledgers and inventory systems, intentionally forego a bit of availability during partitions, while systems that cannot tolerate downtime might settle for eventual consistency instead.
OLTP vs. OLAP workloads
Distributed transactional databases are generally optimized for online transaction processing (OLTP) workloads rather than heavy analytics. OLTP involves many short, frequent transactions that each typically affect a small amount of data, such as inserting a row, updating a few related records, or retrieving a specific item by key. These systems emphasize low latency and high throughput for concurrent users, so each individual transaction is processed quickly and reliably. OLTP databases usually enforce strict data integrity and ACID compliance for each transaction, often breaking data into tables to prevent errors or inconsistencies when data is updated.
The primary goal is to process each operation accurately and swiftly so that business continues in real time without interruption. For instance, when you place an online order, the system records your order, decreases the inventory count, and perhaps updates your loyalty points, all as one unit. Distributed OLTP databases scale this to thousands or millions of transactions per second across clusters of machines, all while preserving consistency.
In contrast, online analytical processing (OLAP) refers to databases or data warehouses designed for complex queries and bulk analysis on large datasets, primarily for insights and decision support. OLAP workloads are predominantly read-intensive: After periodic batch loading of data, users run large SELECT queries with aggregations and joins that scan through massive volumes of records. These systems favor denormalized or pre-aggregated data models to improve read performance, and they tolerate query latency of seconds or even minutes for a complex report because the focus is on deep analysis, not instant transaction processing.
For example, an OLAP query might analyze a year’s worth of sales data to find trends, scanning millions of rows, something an OLTP system is not designed to do frequently. OLAP databases often gather data from many OLTP sources into a central data warehouse as part of an organization’s data warehousing strategy, and use schemas such as star or snowflake that duplicate some data for faster reads. As a result, OLAP systems excel at providing multi-dimensional analytical views and historical reports, but they are not typically used for transaction processing.
In practice, organizations use both types: OLTP systems handle transactional updates on live operational data, and OLAP systems process the historical data for trends and business intelligence. Distributed transactional databases fall into the OLTP camp. They are the backbone for applications that need real-time updates and strict consistency, while distributed analytical platforms such as distributed SQL data warehouses or Hadoop/Spark clusters handle big-picture analysis with different performance trade-offs.
Use cases and benefits
Organizations use distributed transactional databases when applications require both high scalability and strict transactional correctness. For such scenarios, using a distributed transaction solution such as a distributed transactional database means updates spanning multiple services or shards remain atomic and consistent.
A classic example is a financial system that handles many transactions, such as banking transfers, payment processing, and stock trades across regions in real time. In a bank, for example, transferring money between accounts in different data centers is a distributed transaction. The debit in one account and the credit in another must either both succeed or both fail, even if they reside on different servers. Today’s distributed databases perform such multi-node transactions while guaranteeing that once a transfer is committed on one node, that new state is durably recorded and reflected on all other relevant nodes.
Similarly, e-commerce platforms benefit from distributed transactions. Placing an order might involve reserving inventory in one service, charging a payment in another, and creating an order record in a central database. All these actions should be atomic so a customer is not charged without an order being recorded, or an item isn’t double-sold. A distributed transactional database updates the inventory, payment, and order data, possibly on different shards or microservices, in one ACID transaction, keeping the whole system consistent.
Distributed transactional databases scale out far beyond the limits of one machine while maintaining a unified view of the data. By partitioning data across nodes, these databases handle large datasets and high throughputs by simply adding more servers as application usage grows.
At the same time, features such as synchronous replication and failover provide high availability. Even if one server or an entire site goes down, the database continues operating, after a brief failover, without losing any committed data. This makes them robust for enterprise applications. Distributed deployment also offers geo-replication benefits: data replicated to multiple geographic locations means users in each region get low-latency access to nearby data. Transactions even span regions when necessary, such as a user in Europe updating data visible in the U.S., with the system keeping them consistent.
Another benefit is that developers rely on strong consistency features instead of writing complex compensation logic in the application. Without a transactional database, an application might have to implement elaborate checks or rollback procedures to handle partial failures and avoid issues such as double updates or lost updates. With ACID transactions, much of this logic is handled by the database; if any part of the transaction fails, the whole operation is rolled back automatically, simplifying application code. In summary, distributed transactional databases provide a foundation for data integrity at scale: They let businesses increase their data and traffic volume without compromising on correctness.
Challenges and trade-offs
Despite their advantages, using and designing distributed transactional databases is challenging. One major difficulty in distributed transaction management is the performance cost: Coordinating a transaction across multiple nodes and replicas introduces extra network round-trips and waiting. Distributed transactions add complexity and latency; every participating node has to coordinate, and a slow or unresponsive participant delays all of them. In effect, the transaction’s speed is often limited by the slowest link in the chain.
For high-volume use cases, this coordination overhead means throughput might not scale linearly with the number of nodes if many transactions span multiple partitions. The system also needs to hold various locks or version records until a distributed transaction is complete, which means the system handles fewer transactions at a time compared with single-node operations. In particular, long transactions that keep resources locked while waiting for remote work to finish reduce throughput. In practice, achieving sub-millisecond response times becomes harder when messages must be exchanged between nodes for each commit.
Another challenge is fault tolerance and recovery during transactions. Multiple nodes mean more points of failure; a crash or network glitch affecting any participant or the coordinator jeopardizes the transaction. The database uses safeguards to preserve data integrity if something fails, such as aborting incomplete transactions.
Protocols such as 2PC are robust but not infallible. For example, if the coordinator crashes after participants have prepared, those participants will be left waiting until a new coordinator or backup confirms the outcome. Likewise, if a data node fails after committing its part, other nodes need to know to commit as well and not roll back. These situations require careful handling and need write-ahead logs, timeouts, and automated failover procedures to avoid stuck transactions or deadlocks in the system. In the worst case, such as a permanent coordinator failure at the wrong time, unblocking the system might require manual intervention or specialized algorithms.
One common alternative approach is the Saga pattern, which breaks a multi-step workflow into a series of local transactions with corresponding compensating transactions to undo the changes made by each step if something fails. This avoids having one locking global coordinator and favors eventual consistency over immediate consistency. For example, instead of a 2PC across inventory, payment, and order services, an e-commerce application might create an order, then trigger a payment service, then update inventory; if any step fails, subsequent compensating actions, such as refunding the payment or canceling the order, attempt to reverse the earlier steps. The trade-off is that writing data to multiple nodes within a single atomic transaction adds latency and potential blocking, but it makes error handling simpler from the application’s perspective when it succeeds. During some failures, the system may become temporarily unavailable or read-only to stay consistent, reflecting the earlier CAP trade-off. In other words, the database might need to refuse new transactions until it can safely recover, which is a hit to availability but ensures correctness.
Operational complexity is another factor to consider. Running a distributed transactional database often requires expert configuration and monitoring. Tasks such as re-sharding data, adding or removing nodes, and upgrading software versions without downtime aren’t easy. Inter-node coordination means network latency, clock synchronization, and configuration consistency all become important. Debugging issues is harder because an error may involve multiple nodes or only occur under certain network conditions.
From a development perspective, transactions that span distributed data should design their schemas to reduce the number of nodes touched and consequently reduce latency. In some cases, refactoring an application to avoid multi-node transactions, such as by keeping related data together, simplifies the system. Some architects use distributed transactions sparingly, or only for certain parts of the workload, and use simpler eventually-consistent approaches for others. This hybrid strategy reduces bottlenecks while still guaranteeing correctness where it matters.
Overall, implementing a distributed transactional database requires balancing the rigor of ACID rules with the realities of unreliable networks and distributed failures. The platform must coordinate atomic commits across multiple resources, and the engineering team must set up distributed monitoring, tuning, and recovery procedures, a more complex transaction management process than in a single-node system.
Not every application needs this level of guarantee. For some, a one-node database or a sharded database with relaxed consistency is sufficient and easier to manage. But for those use cases that do require strict consistency across a distributed dataset, the challenges are worth overcoming. With continuing advances, such as improved consensus algorithms, faster networks, and better hardware, the performance penalty of distributed transactions is gradually shrinking, making scalable, fault-tolerant, and transactional systems more feasible. The key is understanding the tradeoffs and designing the system to mitigate them as much as possible, through techniques such as data locality, partitioning, and failure handling.
Aerospike’s approach to distributed transactions
Building a distributed transactional database is no small feat, but that’s the challenge Aerospike was designed to overcome. Aerospike’s real-time data platform combines NoSQL speed with relational integrity, offering ACID-compliant transactions across a cluster without the usual tradeoffs. In fact, Aerospike has spent years refining a strong consistency mode, externally validated by Jepsen tests, as the foundation for its multi-record transactions. The result is a database known as one of the fastest and most resilient at scale, with full distributed ACID guarantees. Whether you’re updating one record or coordinating a complex cross-shard transfer, Aerospike keeps every operation correct without sacrificing performance or reliability.
Aerospike offers a proven solution for organizations that need both high scalability and data consistency. Its 8.0 release delivers strict serializability and automatic failover, so data remains accurate and available even under heavy loads and network partitions.
To put this in perspective, one Aerospike customer recently supported nearly 300 million transactions per second during Black Friday traffic, all while maintaining ACID consistency. These real-world results are a testament to Aerospike’s engineering approach. Developers no longer need to write custom saga workflows or worry about partial failures. Aerospike handles distributed commits natively, letting you focus on innovation instead of data plumbing.
Learn more about how Aerospike powers your most demanding transactional workloads with our webinar, Understanding High-Throughput Transactions at Scale, and discover how to future-proof your data architecture with Aerospike’s next-generation distributed database.
What is a distributed transactional database?
A distributed transactional database is a database system that spreads data across multiple nodes or locations while still supporting ACID-compliant transactions that maintain data integrity. In such systems, transactions can involve data on different nodes yet maintain the same guarantees of atomicity, consistency, isolation, and durability that a single-node database does. In effect, a distributed transaction integrates operations on multiple databases, including remote databases across multiple servers in a distributed environment, into one unit, while a local transaction involves only one database. This combination lets applications scale while maintaining strong data integrity. Historically, maintaining a consistent and persistent database state across distributed nodes has been challenging.
Architecture and data distribution
Distributed transactional databases typically use a shared-nothing architecture where data is partitioned (sharded) across many nodes, and each node runs the same database software. Horizontal scaling through partitioning helps the system handle growing data and workload by adding nodes.
It’s important to avoid hotspots by distributing data and workloads evenly so that no one node becomes a bottleneck. Each partition (or shard) holds a subset of the data, and a coordination mechanism maps transactions to the correct nodes responsible for the relevant data. This shared-nothing design means each node operates independently on its partition of the data, relying on network communication for any cross-node operations. The cluster’s distributed data must be carefully partitioned to balance load, and related records are often kept together on the same shard to perform local transactions when possible, reducing multi-node distributed transactions.
Distributed databases also replicate data across multiple nodes to make them more fault tolerant and available. Each data partition often has a primary copy and one or more replicas on different nodes or racks. If one node fails, a replica takes over serving that data, keeping the database up.
Many distributed databases replicate transaction data synchronously: Each write is propagated to replicas at commit time to keep them up-to-date. This means once a transaction is committed on the primary node, the transactional data is durably stored on several nodes, so no data is lost even if a server crashes. Some systems use quorum consensus or other algorithms to coordinate replicas. The tradeoff is that writing data to multiple nodes can add latency, but it makes the system more durable and ensures all users see the same data even after failures.
In practice, running on multiple servers requires robust failure handling: Today’s systems use automatic failover by promoting a replica to primary if a node crashes and rebalancing data to restore redundancy. This way, the distributed data storage remains consistent and available, at the cost of some extra latency due to replication overhead.
ACID transactions and strong consistency
A defining characteristic of transactional databases is adherence to the ACID properties of atomicity, consistency, isolation, and durability. In a distributed context, ACID is extended across nodes:
All parts of a distributed transaction either commit or none do (atomicity)
The system moves from one valid state to another according to defined rules (consistency)
Concurrent transactions do not interfere with each other’s intermediate data (isolation)
Once committed, the results survive failures (durability)
Enforcing these properties in a distributed setup is more complex than on one node because multiple machines and network links are involved in each transaction. Each node must support local ACID properties, and the system as a whole must support these properties globally.
Maintaining strong data consistency means every user or application sees the same view of data across the distributed system at any given time. Distributed transactional databases are usually designed as “CP” systems under the CAP theorem: They prioritize consistency and partition tolerance over absolute availability. Consistency prevents anomalies like reading stale or partial data, which is important for transactional workloads that require up-to-date information, such as a financial transfer that must debit and credit accounts in sync. Many earlier NoSQL database models sacrificed ACID guarantees to maximize availability and scalability, resulting in eventual consistency models.
In contrast, today’s distributed transactional systems aim for strict serializability, the strongest isolation level, so transactions appear to execute in one globally ordered sequence. This often requires concurrency control and commit protocols.
Concurrency control and distributed commit
In any transactional database, a concurrency control mechanism provides isolation (the "I" in ACID) so that simultaneous transactions do not corrupt each other’s data. In distributed databases, the challenge is to preserve a global ordering or serializability even though transactions execute on different nodes.
In practice, many systems use techniques such as two-phase locking (2PL) across the cluster or multi-version concurrency control to make sure they perform transactions in the right order. For example, if all participating nodes use strict 2PL for concurrency control, the overall system runs all the transactions in the right order across the cluster. Some of today’s designs use timestamp ordering or synchronized clocks for the same effect without explicit locking by having each transaction carry a globally unique timestamp (essentially a global transaction ID) and committing in timestamp order.
For atomicity across multiple nodes, distributed databases rely on a special commit protocol. The most common is the two-phase commit (2PC) protocol. In 2PC, a central transaction coordinator, often called a transaction manager or distributed transaction coordinator, does this in two steps: It asks all involved nodes to prepare. Each node effectively marks its part of the transaction, its transaction branch, as ready to commit, and to report if it can commit successfully.
If and only if all participants vote “yes” to prepare, the coordinator sends a commit instruction to finalize the transaction on every node.
This two-phase process means either every participant makes the changes permanent or, if any one cannot proceed, all participants abort and roll back their part, maintaining atomicity across the cluster. In other words, all the transaction branches must agree to commit; if any branch fails or votes to abort, the coordinator cancels the entire transaction and initiates rollbacks on every participant, so no partial changes occur.
Variants such as the XA standard in relational databases use a different approach for distributed transactions. During the prepare phase, each node typically records the pending transaction in a log and may hold locks, so that even if a crash occurs, it recovers and either commits or aborts consistently after reboot. Two-phase commit is a foundational transaction solution for distributed systems. It underpins the standard X/Open XA transactions that many enterprise environments use.
While 2PC guarantees a consistent outcome, it introduces additional overhead and latency. All parties must coordinate and wait for each other, so a slow or unresponsive participant delays all the nodes. Moreover, the system must pause between the prepare and commit phases, which adds network round-trips. If the coordinator or any node fails, other nodes might be left waiting and holding locks until the issue is resolved. In such a case, the transaction may be left in an unresolved state, often called an “in-doubt” transaction status, requiring recovery or manual intervention to decide whether to commit or roll back once the systems come back online. This means distributed transactions can be slower and can even block in some failures, unlike local transactions, which either commit or fail quickly. Techniques such as timeouts, automatic failure detection, and consensus algorithms (Paxos/Raft) help mitigate these problems because the database recovers from partial failures. Nevertheless, the complexity of coordinating multiple nodes is a fundamental reason why distributed ACID transactions are more challenging than single-node transactions.
Consistency vs. availability (CAP trade-offs)
The CAP theorem states that any distributed system cannot simultaneously guarantee consistency, 100% availability, and partition tolerance. At best, it performs only two of the three. Distributed transactional databases lean towards CP (consistency + partition tolerance): They view data consistently and operate correctly across network partitions, but they may sacrifice some availability in rare failure cases.
For example, if a network partition occurs between two data centers, a strongly consistent database might halt transactions in one partition, making that partition temporarily unavailable for writes, rather than allow conflicting updates that could lead to an inconsistent state. In contrast, some eventually consistent systems choose AP (availability + partition tolerance), meaning they accept writes on both sides of a partition at the cost of immediate consistency, reconciling differences later.
The classic two-phase commit approach exemplifies the CP choice: It blocks progress if not all nodes agree, which means that during a network issue, the system might become unavailable to preserve consistency. This trade-off is acceptable and often necessary in scenarios where data integrity is paramount. For instance, one would prefer to pause a banking service during a network glitch rather than allow money to “disappear” or be duplicated due to inconsistent updates.
No distributed system can avoid escaping CAP. Even the most advanced distributed databases cannot guarantee both absolute consistency and uninterrupted availability in the face of network partitions. What today’s databases do is optimize for as much availability as possible while being consistent, using techniques such as fast failure detection and leader elections. They improve uptime during common failures, but when a choice must be made, transactional systems will usually support consistency over accepting divergent writes. Indeed, today’s distributed SQL databases explicitly follow the CP model: They use consensus replication such as Paxos or Raft to coordinate writes and will pause updates, or degrade to read-only mode, during a partition or primary failure rather than compromise data consistency.
Understanding this CAP trade-off is important when designing or choosing a distributed database. Systems that require strong correctness, such as financial ledgers and inventory systems, intentionally forego a bit of availability during partitions, while systems that cannot tolerate downtime might settle for eventual consistency instead.
OLTP vs. OLAP workloads
Distributed transactional databases are generally optimized for online transaction processing (OLTP) workloads rather than heavy analytics. OLTP involves many short, frequent transactions that each typically affect a small amount of data, such as inserting a row, updating a few related records, or retrieving a specific item by key. These systems emphasize low latency and high throughput for concurrent users, so each individual transaction is processed quickly and reliably. OLTP databases usually enforce strict data integrity and ACID compliance for each transaction, often breaking data into tables to prevent errors or inconsistencies when data is updated.
The primary goal is to process each operation accurately and swiftly so that business continues in real time without interruption. For instance, when you place an online order, the system records your order, decreases the inventory count, and perhaps updates your loyalty points, all as one unit. Distributed OLTP databases scale this to thousands or millions of transactions per second across clusters of machines, all while preserving consistency.
In contrast, online analytical processing (OLAP) refers to databases or data warehouses designed for complex queries and bulk analysis on large datasets, primarily for insights and decision support. OLAP workloads are predominantly read-intensive: After periodic batch loading of data, users run large SELECT queries with aggregations and joins that scan through massive volumes of records. These systems favor denormalized or pre-aggregated data models to improve read performance, and they tolerate query latency of seconds or even minutes for a complex report because the focus is on deep analysis, not instant transaction processing.
For example, an OLAP query might analyze a year’s worth of sales data to find trends, scanning millions of rows, something an OLTP system is not designed to do frequently. OLAP databases often gather data from many OLTP sources into a central data warehouse as part of an organization’s data warehousing strategy, and use schemas such as star or snowflake that duplicate some data for faster reads. As a result, OLAP systems excel at providing multi-dimensional analytical views and historical reports, but they are not typically used for transaction processing.
In practice, organizations use both types: OLTP systems handle transactional updates on live operational data, and OLAP systems process the historical data for trends and business intelligence. Distributed transactional databases fall into the OLTP camp. They are the backbone for applications that need real-time updates and strict consistency, while distributed analytical platforms such as distributed SQL data warehouses or Hadoop/Spark clusters handle big-picture analysis with different performance trade-offs.
Use cases and benefits
Organizations use distributed transactional databases when applications require both high scalability and strict transactional correctness. For such scenarios, using a distributed transaction solution such as a distributed transactional database means updates spanning multiple services or shards remain atomic and consistent.
A classic example is a financial system that handles many transactions, such as banking transfers, payment processing, and stock trades across regions in real time. In a bank, for example, transferring money between accounts in different data centers is a distributed transaction. The debit in one account and the credit in another must either both succeed or both fail, even if they reside on different servers. Today’s distributed databases perform such multi-node transactions while guaranteeing that once a transfer is committed on one node, that new state is durably recorded and reflected on all other relevant nodes.
Similarly, e-commerce platforms benefit from distributed transactions. Placing an order might involve reserving inventory in one service, charging a payment in another, and creating an order record in a central database. All these actions should be atomic so a customer is not charged without an order being recorded, or an item isn’t double-sold. A distributed transactional database updates the inventory, payment, and order data, possibly on different shards or microservices, in one ACID transaction, keeping the whole system consistent.
Distributed transactional databases scale out far beyond the limits of one machine while maintaining a unified view of the data. By partitioning data across nodes, these databases handle large datasets and high throughputs by simply adding more servers as application usage grows.
At the same time, features such as synchronous replication and failover provide high availability. Even if one server or an entire site goes down, the database continues operating, after a brief failover, without losing any committed data. This makes them robust for enterprise applications. Distributed deployment also offers geo-replication benefits: data replicated to multiple geographic locations means users in each region get low-latency access to nearby data. Transactions even span regions when necessary, such as a user in Europe updating data visible in the U.S., with the system keeping them consistent.
Another benefit is that developers rely on strong consistency features instead of writing complex compensation logic in the application. Without a transactional database, an application might have to implement elaborate checks or rollback procedures to handle partial failures and avoid issues such as double updates or lost updates. With ACID transactions, much of this logic is handled by the database; if any part of the transaction fails, the whole operation is rolled back automatically, simplifying application code. In summary, distributed transactional databases provide a foundation for data integrity at scale: They let businesses increase their data and traffic volume without compromising on correctness.
Challenges and trade-offs
Despite their advantages, using and designing distributed transactional databases is challenging. One major difficulty in distributed transaction management is the performance cost: Coordinating a transaction across multiple nodes and replicas introduces extra network round-trips and waiting. Distributed transactions add complexity and latency; every participating node has to coordinate, and a slow or unresponsive participant delays all of them. In effect, the transaction’s speed is often limited by the slowest link in the chain.
For high-volume use cases, this coordination overhead means throughput might not scale linearly with the number of nodes if many transactions span multiple partitions. The system also needs to hold various locks or version records until a distributed transaction is complete, which means the system handles fewer transactions at a time compared with single-node operations. In particular, long transactions that keep resources locked while waiting for remote work to finish reduce throughput. In practice, achieving sub-millisecond response times becomes harder when messages must be exchanged between nodes for each commit.
Another challenge is fault tolerance and recovery during transactions. Multiple nodes mean more points of failure; a crash or network glitch affecting any participant or the coordinator jeopardizes the transaction. The database uses safeguards to preserve data integrity if something fails, such as aborting incomplete transactions.
Protocols such as 2PC are robust but not infallible. For example, if the coordinator crashes after participants have prepared, those participants will be left waiting until a new coordinator or backup confirms the outcome. Likewise, if a data node fails after committing its part, other nodes need to know to commit as well and not roll back. These situations require careful handling and need write-ahead logs, timeouts, and automated failover procedures to avoid stuck transactions or deadlocks in the system. In the worst case, such as a permanent coordinator failure at the wrong time, unblocking the system might require manual intervention or specialized algorithms.
One common alternative approach is the Saga pattern, which breaks a multi-step workflow into a series of local transactions with corresponding compensating transactions to undo the changes made by each step if something fails. This avoids having one locking global coordinator and favors eventual consistency over immediate consistency. For example, instead of a 2PC across inventory, payment, and order services, an e-commerce application might create an order, then trigger a payment service, then update inventory; if any step fails, subsequent compensating actions, such as refunding the payment or canceling the order, attempt to reverse the earlier steps. The trade-off is that writing data to multiple nodes within a single atomic transaction adds latency and potential blocking, but it makes error handling simpler from the application’s perspective when it succeeds. During some failures, the system may become temporarily unavailable or read-only to stay consistent, reflecting the earlier CAP trade-off. In other words, the database might need to refuse new transactions until it can safely recover, which is a hit to availability but ensures correctness.
Operational complexity is another factor to consider. Running a distributed transactional database often requires expert configuration and monitoring. Tasks such as re-sharding data, adding or removing nodes, and upgrading software versions without downtime aren’t easy. Inter-node coordination means network latency, clock synchronization, and configuration consistency all become important. Debugging issues is harder because an error may involve multiple nodes or only occur under certain network conditions.
From a development perspective, transactions that span distributed data should design their schemas to reduce the number of nodes touched and consequently reduce latency. In some cases, refactoring an application to avoid multi-node transactions, such as by keeping related data together, simplifies the system. Some architects use distributed transactions sparingly, or only for certain parts of the workload, and use simpler eventually-consistent approaches for others. This hybrid strategy reduces bottlenecks while still guaranteeing correctness where it matters.
Overall, implementing a distributed transactional database requires balancing the rigor of ACID rules with the realities of unreliable networks and distributed failures. The platform must coordinate atomic commits across multiple resources, and the engineering team must set up distributed monitoring, tuning, and recovery procedures, a more complex transaction management process than in a single-node system.
Not every application needs this level of guarantee. For some, a one-node database or a sharded database with relaxed consistency is sufficient and easier to manage. But for those use cases that do require strict consistency across a distributed dataset, the challenges are worth overcoming. With continuing advances, such as improved consensus algorithms, faster networks, and better hardware, the performance penalty of distributed transactions is gradually shrinking, making scalable, fault-tolerant, and transactional systems more feasible. The key is understanding the tradeoffs and designing the system to mitigate them as much as possible, through techniques such as data locality, partitioning, and failure handling.
Aerospike’s approach to distributed transactions
Building a distributed transactional database is no small feat, but that’s the challenge Aerospike was designed to overcome. Aerospike’s real-time data platform combines NoSQL speed with relational integrity, offering ACID-compliant transactions across a cluster without the usual tradeoffs. In fact, Aerospike has spent years refining a strong consistency mode, externally validated by Jepsen tests, as the foundation for its multi-record transactions. The result is a database known as one of the fastest and most resilient at scale, with full distributed ACID guarantees. Whether you’re updating one record or coordinating a complex cross-shard transfer, Aerospike keeps every operation correct without sacrificing performance or reliability.
Aerospike offers a proven solution for organizations that need both high scalability and data consistency. Its 8.0 release delivers strict serializability and automatic failover, so data remains accurate and available even under heavy loads and network partitions.
To put this in perspective, one Aerospike customer recently supported nearly 300 million transactions per second during Black Friday traffic, all while maintaining ACID consistency. These real-world results are a testament to Aerospike’s engineering approach. Developers no longer need to write custom saga workflows or worry about partial failures. Aerospike handles distributed commits natively, letting you focus on innovation instead of data plumbing.
Learn more about how Aerospike powers your most demanding transactional workloads with our webinar, Understanding High-Throughput Transactions at Scale, and discover how to future-proof your data architecture with Aerospike’s next-generation distributed database.