Real-Time Search APIs: Managing Latency at Scale

Real-time search APIs make new data searchable in seconds, which is vital for AI tools, live event systems, and platforms demanding instant results. However, managing latency - how long it takes for data to be indexed or retrieved - is a challenge, especially at scale. This article explains how to reduce delays, measure performance accurately, and build systems that stay fast under heavy load. Key takeaways include:

• Latency Sources: Delays come from network transit, query complexity, indexing overhead, and large payloads.
• Measuring Latency: Focus on p95/p99 metrics to identify the slowest user experiences.
• Optimization Strategies: Use caching, parallel processing, efficient queries, and reduce network distance.
• Failure Management: Implement retry limits, adaptive throttling, and prioritize critical tasks during high traffic.

How to Optimize API Performance for High-Traffic Applications | Keyhole Software

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Latency Basics in Real-Time Search APIs

Real-time search systems need to strike a balance between speed and scalability to maintain responsiveness in AI-driven applications. Understanding the basics of latency is key to pinpointing delays and finding ways to reduce them.

What Causes Latency

API latency refers to the time it takes from sending a request to receiving a complete response. It consists of two main components: queue time (time spent in transit and in the processing queue) and service time (the time the server spends actively processing the request).

When a search request is made, it goes through several stages where delays can build up. These include DNS resolution, the TCP connection handshake, travel time across the network (which increases with geographic distance), query processing (potentially involving thousands of documents), and response formatting and serialization. Additionally, background activities like shard merging or heavy indexing can create spikes in query latency.

Breaking down these stages helps in accurately measuring and addressing latency issues.

How to Measure Latency

Average response times can be misleading because they don’t reveal the full picture. For example, while the average response time might be 500ms, some users could experience delays of up to 5 seconds. This is why measuring percentile latencies is essential. Metrics like p50 (median) show what half of users experience, while p95 highlights delays for the slowest 5%, and p99 identifies issues affecting the slowest 1% of requests.

Another key metric is Time to First Byte (TTFB), which measures how quickly the server starts sending a response. This is especially important for streaming AI systems. Additionally, throughput, measured in Queries Per Second (QPS) or Queries Per Minute (QPM), shows how many requests the system can handle before performance degrades. In distributed search systems, tracking latency across phases (e.g., can_match, query, fetch, and expand) at the coordinator-node level provides a more comprehensive view of the request lifecycle than monitoring individual shards.

"Responsiveness is core to any search solution, and communication delays between search requests and responses can present problems for your users."

• David Zane, Software Engineer, AWS

Accurate metrics are critical for identifying where delays occur, which leads us to the common sources of latency.

Where Delays Happen Most

Certain inefficiencies can significantly impact query performance. For instance, poorly designed queries with overly complex filters or suboptimal logic can force the system to scan unnecessary documents, slowing down the query phase. Similarly, the fetch phase can drag when retrieving document content, especially if payloads are too large or batch sizes are excessive.

Network congestion is another major factor. For example, while a server might process a query in just 21ms, the total time could balloon to 125ms due to 104ms spent in network transit. Hosting your application in a different cloud region from your search index can further increase these delays.

Oversized response payloads are another culprit. Including large vector values in responses (e.g., using include_values=true) requires additional time to retrieve these values from object storage, adding to latency. Finally, searchable latency - the delay before newly ingested data becomes searchable - is influenced by your index's refresh_interval. Setting this interval too low increases indexing overhead, while setting it too high delays the availability of fresh data.

Building Low-Latency Systems with Rate Limits

Rate limits are essential to safeguard API infrastructure from overuse, but they can pose challenges when creating fast, responsive systems. The trick is to work within these constraints by using smarter request patterns, optimizing payloads, and employing caching strategies. This way, you can stay within the limits while keeping latency to a minimum.

How Rate Limits Impact Latency

Rate limits generally fall into two categories: per-second caps and burst limits. Hitting these limits can lead to delayed or rejected requests, often marked by an HTTP 429 response. This increases overall latency. A common way to handle this is by implementing exponential backoff - retrying requests with delays of 1, 2, and 4 seconds, respectively.

The problem can worsen when multiple components share the same rate limit. For instance, if an AI system runs several parallel search queries and all exceed the limit, you'll face multiple backoff sequences running simultaneously, further complicating latency management.

Making Requests More Efficient

One often-overlooked area for improvement is connection management. Reusing TCP connections can save time by skipping the handshake process for every request, which significantly cuts down on latency.

"To avoid going through this [TCP connection] process on every request, and reduce average request latency, cache and reuse the index connection object whenever possible."

• Pinecone Documentation

Another quick win is reducing payload size. For example, if you’re querying a vector database but don’t need the vector values in the response, set include_values=false. Large vector arrays increase transfer time, so excluding unnecessary data can make a big difference. Similarly, leaving out metadata fields that your application doesn’t actually use can speed things up.

Targeted querying is another way to improve efficiency. By using namespaces and metadata filters, you can narrow your search scope before running the query. This reduces the computational load for each API call and helps you stay within rate limits. For example, if you’re analyzing product reviews, filtering by product category first can significantly reduce the workload compared to scanning the entire review database.

A great case study comes from Microsoft, which, in May 2024, optimized a document processing app to reduce response times from 315 seconds to just 3 seconds - a staggering 105x improvement. Their strategy involved compressing generation tokens and avoiding redundant text rewriting by appending documents via code rather than relying solely on the language model.

"The speed of the person (the model used) and the amount they need to type (the number of generation tokens) tend to be the largest contributor to long response times."

• Luca Stamatescu, Microsoft

These strategies lay the groundwork for further efficiency gains through caching.

Scaling Architecture for Low Latency

Once you've fine-tuned individual requests, the next hurdle is keeping that speed intact as your system expands. This means building systems that can handle thousands of simultaneous queries without slowing down. This is particularly vital when implementing AI search capabilities that require high computational power. By extending earlier strategies for efficient requests, scaling your architecture ensures that even under heavy loads, latency remains low.

Running Queries in Parallel

Modern search engines rely on a method called the scatter-gather pattern to process queries across distributed systems. Here's how it works: when a search request comes in, it’s sent out to all content nodes simultaneously to identify local top hits. Then, only the global best results are retrieved with full document summaries. This two-step process avoids wasting bandwidth on unnecessary data.

"By doing the query in two protocol phases one avoids transferring summary data for hits which will not make it into the global best hits."

• Vespa Serving Scaling Guide

Multithreaded execution takes this concept even further by allowing a single query to run across multiple CPU cores. The trick is to align your thread count with your available cores - too many threads can create unnecessary overhead. Systems that involve heavy document processing, often called "dynamic query work", benefit the most from parallelization. In cases where administrative overhead is the main bottleneck, grouped distribution becomes more effective. This involves replicating document collections across node groups and balancing the query load.

A great real-world example of parallel processing comes from Twitter (now X). In June 2020, engineers Nico Tonozzi and Dumitru Daniliuc revamped their search indexing system. They swapped out unrolled linked lists for concurrent skip lists, enabling out-of-order document insertion without waiting for sorting. By removing a multi-second ingestion buffer and adopting a 31-bit document ID scheme (with 27 bits for millisecond-level timestamps), they cut indexing latency from 15 seconds to just 1 second. This meant real-time updates - like X posts search results and profile changes - showed up instantly.

Parallel processing is powerful, but reducing physical network distance can also make a big difference in latency.

Reducing Network Distance

Geography plays a surprisingly big role in latency. Hosting your application in the same cloud region as your search index can significantly reduce round-trip times. Every additional network hop adds milliseconds, which can quickly add up when you're handling thousands of requests per second.

For systems with users spread across multiple regions, grouped content distribution ensures queries are routed to the nearest node group. This keeps response times consistent regardless of where requests originate. Private connectivity options, like dedicated network links, bypass the public internet entirely, cutting both latency and security risks.

Another simple but effective optimization: directly target indexes by their DNS host in production. This skips the "describe index" API call, eliminating an unnecessary round-trip and reducing the risk of failure. Also, connection pooling - reusing existing TCP connections instead of creating new ones for every request - avoids the delays caused by repeated handshakes.

While network optimizations ensure fast data transfer, handling failures gracefully is just as important to maintain performance.

Handling Failures Without Slowdowns

Even with perfect parallelism and network design, failure management is critical. The goal is to prevent failing components from dragging down the entire system. Adaptive throttling helps by having clients monitor their success rates. If the backend starts rejecting requests, clients can drop some requests locally, saving the backend from wasting resources on rejections.

Criticality-based shedding prioritizes essential requests over less important ones during high traffic. For example, a system might temporarily discard non-critical tasks like search suggestions while focusing on core search functions. Google's approach to adaptive throttling uses a 2x multiplier, allowing clients to send requests until they reach twice the number of accepted requests. This helps detect when the backend is recovering.

Deadline propagation ensures no time is wasted by passing absolute time limits through the entire request stack. Pair this with a per-client retry budget - limiting retries to 10% of total traffic - and you can prevent a failure from causing a massive spike in retries. This keeps the retry volume increase to just 1.1x instead of 3x. Adding randomized jitter to exponential backoff delays stops large groups of clients from retrying at the same time. Lastly, switching from First-In-First-Out (FIFO) to Last-In-First-Out (LIFO) queue management during congestion ensures the freshest requests - those most likely to still be valid - are processed first.

Monitoring and Improving Performance

Creating a fast system is a great start, but keeping it fast is an ongoing challenge. Without consistent monitoring, performance issues can creep in unnoticed until they start affecting users. The key is to track the right metrics, test under realistic conditions, and use tools that provide clear insights into how your system behaves.

Tracking Latency Metrics

Looking at average response times often doesn’t tell the whole story. To get a clearer picture of what users are experiencing, focus on tail latencies like p95 and p99. These metrics highlight the delays faced by the slowest users - issues that averages tend to mask. As Ravi Teja Thutari, Lead Software Engineer, explains:

"Focusing on p95 latency (and above) is crucial. It highlights the worst-case delays that real users feel".

For example, during a July 2025 project for a travel search engine, Ravi’s team discovered through p95 metrics that some users were waiting 10 seconds for results, even though the average latency seemed fine. By introducing a Redis caching layer and tweaking Google Cloud Run settings (increasing concurrency to 5 and setting minimum instances to 1), they cut the p95 latency down to 2 seconds - a massive improvement.

Here’s a quick summary of key metrics to track:

Metric	What It Reveals	Why It Matters
p95/p99 Latency	Worst-case user experience	Highlights delays for the slowest users
Phase-Specific Time	Where delays occur	Pinpoints issues in retrieval, processing, or enrichment
Throttling Rate	Capacity constraints	Shows when rate limits are blocking requests
Service Time	Total request-to-response duration	Includes network overhead and serialization

Once you’re tracking these metrics, test them under conditions that mimic real-world usage.

Testing Under Load

Real-world traffic is unpredictable. Load testing needs to account for geographic variety, different network speeds, sudden traffic spikes, and edge cases. Test from multiple locations using varied connection types like 4G, high-speed Wi-Fi, and LAN to uncover latency issues that might only surface in specific scenarios. The stakes are high: Amazon found that every additional 100ms of latency costs them 1% in profit, while Google experienced a 20% traffic drop due to just a half-second delay.

Different types of load tests can uncover various vulnerabilities:

• Peak Testing: Simulates high-traffic events, such as product launches.
• Stress Testing: Pushes the system beyond its capacity to find breaking points.
• Spike Testing: Introduces sudden traffic surges to test resilience during viral moments.
• Endurance Testing: Runs for extended periods to identify gradual issues, like memory leaks.

For serverless systems, cold starts can be a bottleneck. Simulate these by adding random delays during load tests. Keep an eye on resource usage during testing - if CPU usage consistently exceeds 80% or RAM maxes out, you’ve likely found a bottleneck.

Armed with this data, you can proactively address performance challenges.

Using Desearch's Monitoring Tools

Desearch provides tools to help manage performance issues in real time. Its developer dashboard offers clear insights into system behavior, capturing every API request with precise timings. This allows you to identify slow queries as they occur. The platform’s error tracking system flags failed requests and rate-limit rejections, helping teams spot patterns before they escalate into major problems.

Metrics like request volume, response times, and error rates are displayed on a user-friendly dashboard, eliminating the need to sift through raw logs or build custom monitoring solutions. For teams running AI agents that send thousands of search requests, this level of visibility is critical for maintaining consistent performance as usage grows. It complements strategies like rate limiting and caching, ensuring your system stays fast and reliable.

Key Takeaways

Creating a low-latency real-time search system involves making smart trade-offs to achieve noticeable performance improvements. The approach is simple: reuse connections to avoid the repeated overhead of TCP handshakes, deploy close to your data to minimize network distance, and use effective caching to cut down on redundant API calls. As discussed earlier, identifying bottlenecks - like swapping unrolled linked lists for skip lists - can bring significant latency reductions. Below is a breakdown of the core principles that can help you optimize for low latency.

Core Design Principles

The strategies mentioned earlier can be distilled into a set of actionable principles for reducing latency:

• Reduce round trips: Combine requests wherever possible and exclude unnecessary data. For instance, skip large vector values in responses if they aren't needed.
• Partition data intelligently: Use namespaces or metadata filters to ensure queries only scan relevant records instead of the entire dataset.
• Decouple data ingestion: Separate critical fields that need immediate indexing from metadata updates that can be processed asynchronously, such as resolved URLs.

When it comes to pipelines, increasing worker threads and opting for smaller batch sizes (64–256 records) can help reduce queuing delays in I/O-bound systems. Smaller batches might generate more requests, but they also lower overall latency - a worthwhile compromise when speed is the priority. Adjust your index refresh interval to around one second to strike a balance between making data quickly searchable and avoiding the overhead of frequent segment updates. Finally, always implement retry logic with exponential backoff to handle rate limits effectively and prevent system failures.

How Desearch Helps

Desearch applies these principles to deliver top-notch real-time search API performance. It takes care of connection management, data retrieval, and error handling, so you don’t have to build a custom infrastructure from scratch. Its developer dashboard offers live insights into request volumes, response times, and error rates, enabling you to quickly identify and address slow queries or rate-limit issues before they escalate.

For AI agents processing thousands of search requests - whether pulling data from the web, X (Twitter), or multiple sources - this level of monitoring is invaluable. Desearch’s low-latency design and structured JSON outputs ensure your system remains fast and efficient, even as it scales. By following these principles, you can achieve the performance needed for modern AI-driven applications.