Introduction to high throughput

High throughput refers to a system’s ability to process a large amount of work or data in a given amount of time. In computing terms, it is the volume of operations or data transfer completed per second (or other time unit) by a system. For example, a web server’s throughput might be measured in requests handled per second, while a network link’s throughput could be in gigabits of data transmitted per second. A system with high throughput handles many operations quickly, which is important in scenarios ranging from busy e-commerce websites to real-time data analytics. The higher the throughput, the more work the system does in a given timeframe, indicating greater capacity and performance.

Differentiating throughput from latency

Throughput is often discussed alongside latency, but they represent different performance aspects. Latency is the delay or response time for one request or data packet, or how long one operation takes from start to finish. Throughput, on the other hand, is about volume, or how many such operations it handles in a time period. In many systems, there is a tradeoff between the two: Improving one affects the other. For instance, processing tasks in batches increases throughput, but it may introduce a slight delay to each individual task or increase its latency, because the system waits to accumulate a batch. Conversely, handling every request immediately reduces latency but limits throughput if the system becomes overwhelmed. 

Ideally, high-performance systems aim for both low latency and high throughput, meaning each operation is fast, and many operations run concurrently. However, achieving this balance is challenging, and system designers often compromise based on application needs. In summary, latency affects the speed of one operation, while throughput reflects the system’s ability to handle many operations; a network or application with low latency but low throughput might respond quickly to single users but struggle under heavy load, while one with high throughput but high latency handles volume but may feel sluggish to each user.

Why high throughput matters

High throughput is important in system design because it affects a system’s capacity to serve users and process data. A system with greater throughput accommodates more users, transactions, or data flow simultaneously, leading to better scalability and efficiency. In practical terms, higher throughput means more work gets done with the same time and resources, which improves user experience and business outcomes. 

For example, a network with high throughput and low latency feels snappy and handles heavy traffic without congestion, while one with low throughput becomes a bottleneck under load. In enterprise settings, high throughput systems support real-time analytics, large-scale web services, and big data processing requirements without breaking performance. In fact, certain applications require high throughput to function. These include live video streaming, Internet of Things (IoT) data ingestion, and high-performance computing, which require networks and databases that meet throughput thresholds to run well. 

Ultimately, maximizing throughput is about supporting growth: As data volumes and user counts climb, systems engineered for high throughput scale to meet demand, maintain responsiveness, and avoid becoming the limiting factor in an organization’s success. High throughput also saves money, because doing more work with fewer servers or in less time is more efficient. 

Factors that affect throughput

Getting high throughput depends on a variety of factors across hardware, network, and software. Here are some of the factors that influence a system’s throughput:

Hardware and infrastructure capabilities

Raw processing power of servers and devices affects throughput. Faster CPUs with more cores execute more instructions and handle more tasks in parallel, completing more operations per second. Similarly, sufficient memory (RAM) is important; if a system runs out of memory and begins swapping to disk, throughput will plummet. The speed of storage drives matters as well: solid-state drives (SSDs) or NVMe storage can perform input/output much faster than traditional spinning disks, allowing more read/write operations per second and so boosting data processing throughput. 

In essence, robust hardware, including powerful processors, ample fast memory, and quick storage, provides the foundation for high throughput by helping the system handle heavy workloads without becoming CPU- or I/O-bound.

Network capacity and bandwidth

For distributed systems or any application that communicates over a network, the network’s capabilities limit throughput. Network bandwidth defines the maximum data volume that can be transmitted over the connection per second, so higher bandwidth generally allows higher throughput up to that limit. If your network link is saturated or at full capacity, it doesn’t matter how fast your servers are; throughput won’t increase without more bandwidth. 

Network congestion is another factor; when many devices send data simultaneously and overwhelm the network, packets get delayed or dropped, reducing throughput. This is why large systems use high-capacity network infrastructure and techniques such as traffic management and congestion control to maintain throughput under heavy load. Reducing unnecessary network overhead, such as by using efficient protocols and avoiding chatty communication patterns, also helps. 

In short, a well-provisioned and optimized network allows data to flow freely, helping the system as a whole to provide high throughput.

Concurrency and parallelism

Handling many operations at the same time improves throughput. Concurrency, or interleaving multiple tasks, and parallelism, or executing tasks simultaneously on multiple processors or machines, are essential for increasing throughput. If a system does only one thing at a time, its throughput is limited to that single stream of work. 

That’s why today’s software uses multithreading, asynchronous I/O, and distributed computing to keep many operations running concurrently. For example, a database server might use dozens of threads to serve multiple client queries in parallel, or a map-reduce data processing job might run on hundreds of nodes to crunch data faster. By dividing the workload across available resources, you use the system’s capacity and avoid idle time. Properly implemented concurrency raises throughput; a well-designed distributed system handles more load by adding more nodes, maintaining high throughput as it grows.

The challenge, however, is avoiding contention: If all tasks try to use the same resource, such as a lock, a database record, or a network link at once, they block each other and negate the benefits of parallelism. Software must be designed to reduce bottlenecks, so adding parallelism actually translates into higher throughput.

Software efficiency and overhead

The efficiency of software architecture and communication protocols also plays a role in throughput. Every system has some overhead per operation, whether it’s the overhead of an HTTP request and response, the headers and acknowledgments in a network protocol, or the parsing and processing of a database query. Lightweight protocols and data formats carry out the same work with less overhead, leaving more capacity for payload throughput. 

For instance, a binary protocol with minimal metadata might have higher message throughput than a verbose text-based protocol carrying the same data. Similarly, how data is batched or packaged makes a difference: due to fixed per-packet costs, sending a single large payload might incur less total overhead than sending many tiny packets. 

On the software side, algorithms and code efficiency matter, too. Efficient algorithms complete work using fewer CPU cycles, and non-blocking/asynchronous designs prevent idle waits. Caching frequently used results in memory also reduces repeated work and increases throughput. 

In summary, to improve throughput, systems need to trim unnecessary work and bytes wherever possible. Reducing protocol overhead, choosing efficient algorithms, and improving code all increase the volume of useful work done per second.

Strategies for high throughput

Designing for high throughput often requires a combination of architectural approaches and best practices. Here are several strategies to build systems that handle higher throughputs.

Scaling out and distribution

One of the most powerful ways to increase throughput is to scale out a system across multiple machines or nodes. Instead of relying on one server to handle all requests, distributed architectures spread the workload across a cluster. By partitioning data and traffic, where each server handles a subset of users or a portion of the database, you multiply the total throughput roughly linearly with the number of nodes. 

This approach is fundamental in today’s cloud and distributed system design: Think of web services running on dozens of servers or big data platforms such as Hadoop/Spark dividing tasks over a cluster. A well-designed distributed system gets throughput far beyond the limits of one machine. The key is ensuring that adding more nodes yields a proportional increase in capacity, or good scalability, which involves efficient load balancing and reducing coordination overhead. 

For instance, partitioning a database and using a load balancer to direct each query to the correct node processes transactions in parallel, raising throughput. Many of today’s highest-throughput systems, from search engines to social networks, rely on horizontal scaling.

Parallel processing and concurrency techniques

Software uses parallel processing to use hardware efficiently. This happens at multiple levels: within a single machine, using multi-core CPUs and multithreaded programming, and across machines by distributing tasks. Using multithreading or asynchronous processing within applications reduces I/O waits and idle CPU cores. 

For example, a web server might use an event-driven async model or a thread pool to serve many requests at once, rather than processing them one by one. Similarly, restructure data processing jobs into subtasks that run in parallel, such as dividing a large dataset among worker threads or nodes. These approaches use concurrency to boost throughput, as more work is completed in the same time. 

It’s important to handle thread synchronization and resource sharing carefully, using techniques such as fine-grained locking or lock-free data structures, so threads don’t spend most of their time waiting on each other. When done correctly, increasing concurrency increases throughput with available computing resources, as evidenced by multi-core processors and distributed computing frameworks supporting more operations per second together. 

Improving the network and I/O path

Throughput is often improved by addressing bottlenecks in data transfer and I/O. If network communication is a major part of the system, using faster network interfaces, such as switching from 1 Gbps to 10 Gbps Ethernet, or to InfiniBand in high-performance environments, will raise the ceiling for throughput. Similarly, network improvements such as connection pooling, persistent connections, or protocol enhancements reduce overhead per message. 

For example, enabling HTTP/2 or HTTP/3 allows multiple concurrent transfers on one connection, reduces latency and overhead, and improves web service throughput. 

Another tactic is using content delivery networks (CDN) or edge servers. By caching and serving content closer to users, you not only reduce latency but also offload work from the core servers, increasing the overall throughput of the service because fewer requests have to travel to the origin data center. 

On the storage side, using batch I/O or asynchronous I/O keeps disks and networks busy. For instance, database systems often group multiple writes to flush to disk in one sequential operation, which provides higher write throughput than many small random writes. 

In summary, eliminating I/O bottlenecks, whether by upgrading hardware to faster NICs or disks, increasing bandwidth, or improving data transfer through batching, caching, and compression, is essential for improving throughput.

Asynchronous and pipeline processing

High throughput systems frequently use asynchronous processing, pipelining, and queues to keep workloads flowing smoothly. Instead of doing all the work for a request immediately and sequentially, systems break tasks into stages and use buffering between them. 

For example, a logging system might write incoming logs to an in-memory queue and return immediately, then a background process will actually write them to disk. This decoupling means the front end isn’t blocked and continues accepting more logs, improving throughput. Message queues and streaming platforms such as Kafka and RabbitMQ are often introduced as buffering layers to ingest a high volume of events quickly, then distribute the work to consumer processes that handle these events at their own pace. This smooths bursts in load and prevents slow components from dragging down the system’s throughput by decoupling producers and consumers. 

Pipelining is another technique in which a sequence of processing steps is arranged so that multiple items are in different stages of processing simultaneously, like an assembly line. An example is a processor pipeline executing multiple instructions concurrently at different stages or an HTTP pipeline sending multiple requests without waiting for each response. These asynchronous and pipeline approaches mean every part of the system is working on something as much of the time as possible, thereby increasing overall throughput. 

The tradeoff is added complexity in design and the need to handle eventual consistency or ordering issues if tasks are not completed immediately. Nonetheless, for many high throughput scenarios, these patterns help handle large event streams and request loads efficiently.

High throughput in databases and data systems

In the realm of data management, throughput measures how many transactions or queries a database handles per second. Different types of systems prioritize different kinds of throughput.

OLTP systems and transaction throughput

Online transaction processing (OLTP) systems, such as relational databases behind an e-commerce site or banking system, are designed for many small, rapid operations. Throughput in OLTP is often measured in transactions per second, such as how many orders it processes per second. These systems emphasize handling many concurrent writes and reads with minimal latency, because each transaction is user-facing and time-sensitive. OLTP databases are usually designed with features such as indexing, normalization, and ACID compliance so that each individual operation is fast and correct. They also scale throughput by using techniques such as sharding by partitioning the data across servers and replication, to spread out reads. 

A well-tuned OLTP system processes thousands or even millions of tiny transactions per second, focusing on high concurrency and quick response for each operation. 

For instance, a large online payment network or banking system might require high throughput so each swipe of a card or ATM transaction is processed quickly, even during peak hours. Every millisecond counts in OLTP workloads. These systems aim to complete each database commit as fast as possible and to sustain that performance under heavy multi-user load.

OLAP systems and analytical throughput

Online analytical processing (OLAP) systems, by contrast, handle a smaller number of complex queries rather than millions of simple transactions. OLAP databases and data warehouses are geared toward high throughput reads of large data sets. Workloads are predominantly read-intensive, scanning and aggregating millions or billions of records to answer analytical questions. 

In OLAP, the focus is on maximizing the data volume processed per query, or throughput per query, rather than the number of queries per second. These systems often use columnar storage, parallel processing, and pre-aggregated data for high data throughput, such as crunching terabytes of data to produce a report. Because OLAP queries are resource-intensive, they measure throughput in terms of how much data they analyze within a reasonable time, or how many complex queries run concurrently. OLAP systems tolerate higher latency (a query might run for seconds or minutes) as long as they can push through huge data scans and computations. 

In summary, OLTP strives for high throughput of small transactions with low latency, while OLAP aims for high throughput of data processing, even if each query is slower, to deliver comprehensive analysis. Both types of systems require high throughput, but in different ways, one in terms of transaction volume and the other in terms of data volume.

Tradeoffs and consistency in distributed databases

In distributed database systems, such as NoSQL databases or globally replicated SQL databases, there is often a tradeoff between strict consistency and maximum throughput. According to the CAP theorem, relaxing consistency through methods such as using eventual consistency models helps a system to achieve higher availability and throughput, because the database nodes do not have to coordinate synchronously on every update. 

Many high throughput NoSQL stores choose eventual consistency or tunable consistency, which lets them acknowledge writes and serve reads from any replica without waiting for global consensus, which speeds operations. This approach is seen in systems such as Apache Cassandra, where the design sacrifices some immediacy in data consistency to handle more operations concurrently across a cluster. The result is higher write and read throughput for tasks such as logging, analytics, or social media feeds, where it’s acceptable if a few reads see slightly stale data for a short time. 

On the other hand, databases that enforce strict ACID transactions across distributed nodes, such as two-phase commit, or distributed SQL systems, typically have lower throughput at scale because of the overhead of coordination. 

However, advances are being made to narrow this gap. Some distributed databases claim to achieve strong consistency with less throughput penalty by improving transaction protocols. The bottom line is that system designers must choose the right balance: if improving throughput is the top priority, they use a looser consistency model and shared-nothing architecture, while for full consistency, they accept some limits on throughput.

Real-world applications of high throughput

High throughput isn’t just a theoretical goal. It’s a practical necessity in many industries and applications today. Here are a few examples where high throughput is important. 

• Financial trading and banking: Stock exchanges, payment networks, and high-frequency trading systems must process many transactions in real time. For instance, a major stock exchange such as NASDAQ or the NYSE handles thousands of trades and orders every second, where any slowdown could disrupt markets. Banking systems likewise execute countless ATM withdrawals, transfers, and card swipes concurrently, requiring consistent high throughput for a smooth customer experience.

• E-commerce and online services: Large online retail platforms see surges of user activity during promotions and holidays, when their infrastructure may need to handle tens of millions of requests or checkouts per second. A notable example is Flipkart, a major e-commerce site, which sustained around 95 million transactions per second during peak sale events, with the throughput necessary to serve millions of shoppers simultaneously. This level of throughput means page loads, inventory updates, and order placements need to keep up with user clicks in real time.

• Advertising technology (AdTech): Real-time bidding platforms and ad exchanges need high throughput. When an online ad is displayed, a rapid auction occurs among advertisers, and these systems often handle hundreds of millions of ad impression auctions per second across the globe. For example, the ad tech company Criteo was reported to manage about 950 billion ad placement matches per day, with peak loads reaching about 270 million operations per second during real-time bidding bursts. Such high throughput capability is how personalized ads are served in the fraction of a second it takes for a webpage to load.

• Telecommunications and Internet of Things (IoT): Telecom networks, especially 5G, and Internet-of-Things (IoT) deployments generate massive continuous data streams. A cellular network must carry voice, video, and data for millions of concurrent users, requiring very high throughput to avoid call drops or slow internet speeds. 5G networks, for instance, have more data capacity and device connectivity than previous generations, supporting high throughput to accommodate streaming video and real-time device telemetry. Similarly, IoT sensor networks in smart cities or industrial setups might ingest data from tens of thousands of sensors at once. Only a high throughput data pipeline collects and processes all that information in real time for monitoring or analytics.

These examples highlight how high throughput underpins many services we rely on daily. Whether it’s processing a surge of online orders, delivering a personalized ad, or handling a spike in network traffic, systems built for high throughput keep things running smoothly when volumes are high.

High throughput and Aerospike

High throughput is a cornerstone of today’s system performance, so applications handle workloads efficiently and serve large user populations in real time. By understanding and improving factors that affect throughput, from hardware and network capacity to concurrency and software design, architects design systems that not only perform well in benchmarks but also scale reliably in the real world. This is where Aerospike comes into the picture.

Aerospike is a real-time data platform known for delivering high throughput with low latency at petabyte scale. It has been engineered to maintain high throughput even as data grows, using a distributed architecture and innovations such as its patented hybrid memory storage for performance levels traditional databases cannot match. 

Aerospike’s technology is used by enterprises in AdTech, finance, and telecommunications to meet demanding throughput requirements, from powering global advertising auctions to processing banking transactions with predictable sub-millisecond response times. 

If your business depends on handling large volumes of data and transactions quickly, it’s worth exploring how Aerospike’s database solutions help you meet those challenges.