Mastering Fixed Window Redis Implementation for Rate Limiting

In the intricate tapestry of modern web services, where applications constantly exchange data and microservices orchestrate complex business logic, the unbridled flow of requests can quickly turn into a torrent, overwhelming systems and compromising stability. The digital realm demands resilience, and a fundamental pillar of this resilience is rate limiting. It acts as a sophisticated traffic controller, ensuring that resources are allocated fairly, services remain performant, and malicious activities are kept at bay. Among the various strategies employed for this crucial task, the Fixed Window algorithm stands out for its straightforward yet effective approach, and when paired with the blazing speed and atomic operations of Redis, it forms a formidable defense mechanism. This article delves deep into mastering the implementation of Fixed Window rate limiting using Redis, exploring its nuances, best practices, and its indispensable role, often orchestrated by a robust api gateway.

The Unseen Guardian: Understanding the Imperative of Rate Limiting

The internet is a vast and often chaotic place, a double-edged sword that offers unparalleled connectivity but also exposes services to a barrage of potential threats and inefficiencies. Every api call, every user interaction, every data exchange consumes valuable server resources—CPU cycles, memory, network bandwidth, and database connections. Without proper control, a sudden surge in traffic, whether malicious or accidental, can cripple even the most robust infrastructure. This is where rate limiting steps in, not as a barrier, but as a guardian, meticulously managing the flow of requests to uphold service quality and integrity.

The necessity of rate limiting stems from several critical concerns that developers and system architects must address:

1. Preventing Abuse and Security Threats: At its core, rate limiting is a powerful tool against various forms of abuse. Distributed Denial of Service (DDoS) attacks, brute-force login attempts, credential stuffing, and excessive data scraping are common malicious activities that exploit the absence of request limits. By imposing constraints on how many requests a user or client can make within a specified timeframe, rate limiting significantly mitigates the impact of such attacks, making it more difficult and time-consuming for attackers to achieve their objectives. For instance, a login api might be limited to five attempts per minute from a single IP address, effectively slowing down brute-force attacks and buying time for security systems to detect and block malicious actors.

2. Ensuring Fair Resource Allocation and Service Quality: In a multi-tenant environment or for public-facing APIs, it’s imperative to ensure that one user’s excessive activity doesn't degrade the experience for others. Imagine a scenario where a single power user or an errant script makes thousands of requests per second to a shared api. Without rate limiting, this user could monopolize server resources, leading to increased latency, timeouts, and potential outages for all other legitimate users. Rate limiting enforces a fair usage policy, guaranteeing that every consumer receives a consistent level of service and that critical resources are equitably distributed across the entire user base. This directly contributes to a better user experience and higher customer satisfaction, which are paramount for any successful platform.

3. Cost Control and Operational Efficiency: Cloud infrastructure and many third-party services operate on a pay-per-use model, where costs are directly tied to resource consumption. Excessive, unchecked requests can lead to unexpectedly high operational costs. Rate limiting provides a mechanism to control these expenditures by preventing runaway resource utilization. By setting appropriate limits, businesses can better predict and manage their infrastructure costs, avoiding costly surprises. Furthermore, by reducing the load on backend servers, rate limiting can allow for a smaller, more efficient infrastructure footprint, optimizing resource allocation and reducing the need for constant scaling up.

4. Protecting Downstream Services and External Dependencies: Many applications rely on a complex chain of services, including databases, message queues, and external third-party APIs. These downstream components often have their own rate limits or capacity constraints. An unconstrained upstream api could inadvertently flood these dependencies, causing cascading failures throughout the entire system. Rate limiting at the ingress point—often handled by an api gateway—protects these sensitive downstream services, acting as a crucial buffer and preventing an overload that could otherwise bring down an entire service chain. This is particularly important for integrated systems that interact with external partners or services that charge per api call, where uncontrolled requests can incur significant financial penalties or service interruptions.

5. Data Integrity and Operational Stability: Beyond performance and security, rate limiting plays a role in maintaining data integrity. For example, preventing rapid-fire updates to a single record or controlling the submission rate of forms can help avoid race conditions or data corruption issues that might arise from concurrent, unmanaged operations. It also ensures operational stability by preventing accidental or intentional over-usage from destabilizing backend databases or processing queues, allowing services to maintain their uptime and reliability.

In essence, rate limiting is not merely a technical constraint; it is a strategic business decision that safeguards an organization's digital assets, preserves its reputation, and ensures the sustainable growth of its services. While various algorithms exist to achieve this, the Fixed Window approach, bolstered by the power of Redis, offers an accessible and highly effective starting point for many applications.

Dissecting Rate Limiting Algorithms: A Comparative View

Before diving into the specifics of Fixed Window, it's beneficial to understand the landscape of rate limiting algorithms. Each algorithm has its strengths, weaknesses, and ideal use cases. While our focus remains on Fixed Window, grasping the alternatives provides crucial context for informed decision-making.

Here’s a brief overview of the most common algorithms:

Algorithm	Mechanism	Advantages	Disadvantages	Ideal Use Case
Fixed Window	Divides time into fixed-size windows (e.g., 60 seconds). A counter increments for each request within the window. If the counter exceeds the limit, requests are rejected until the next window starts.	Simple to implement, low overhead, easy to understand.	Susceptible to the "burst problem" at window edges (double the limit can pass in a short period across two windows).	Simple api protection, non-critical services, where occasional bursts are acceptable.
Sliding Window Log	Stores a timestamp for each request made within the window duration. When a new request arrives, it counts timestamps within the current window.	Provides a very accurate and fine-grained view of request rates over a rolling period.	High memory consumption, as it stores every request's timestamp. Can be slow with many requests.	Strict rate limiting requirements where accuracy over time is paramount and memory is not a major constraint.
Sliding Window Counter	Combines elements of Fixed Window and Sliding Window Log. It calculates the request count for the current window by weighing the previous window's count and adding the current window's count.	Offers a good compromise between accuracy and memory efficiency. Smoother rate limiting than Fixed Window.	More complex to implement than Fixed Window. Still has some potential for minor inaccuracies near window edges.	General purpose apis requiring smoother request distribution, balancing performance and accuracy.
Token Bucket	Requests consume "tokens." Tokens are added to a bucket at a fixed rate. If the bucket is empty, requests are rejected. The bucket has a maximum capacity.	Allows for bursts up to the bucket capacity while maintaining an average rate. Simple to reason about.	Can be complex to tune (bucket size, refill rate). Not ideal for ensuring consistent rate over long periods.	apis that require burst tolerance (e.g., photo uploads, intermittent batch jobs).
Leaky Bucket	Requests are added to a queue (the bucket) and processed at a fixed rate, like water leaking from a bucket. If the bucket overflows, new requests are dropped.	Smooths out bursty traffic into a steady stream, preventing backend overload.	Introduces latency due to queuing. Requests can be dropped even if the average rate is low if a burst fills the queue.	System protection where backend stability and consistent processing rate are critical (e.g., message queues).

Each of these algorithms offers distinct advantages for different scenarios, but for many common applications where simplicity, performance, and clear enforcement are paramount, the Fixed Window algorithm, especially when implemented with a high-performance key-value store like Redis, provides an excellent foundation.

Deep Dive into Fixed Window Rate Limiting: Simplicity Meets Power

The Fixed Window algorithm is perhaps the most intuitive and easiest to understand among all rate limiting strategies. Its elegance lies in its simplicity, making it a popular choice for developers looking to implement effective request throttling without excessive complexity. Let's dissect its mechanism, advantages, and inherent challenges.

The Mechanism of Fixed Window Rate Limiting

The core idea behind the Fixed Window algorithm is to divide time into discrete, non-overlapping intervals, or "windows," each of a predefined duration (e.g., 1 minute, 5 minutes, 1 hour). For each window, a counter is maintained for a specific client (identified by IP address, user ID, api key, etc.).

Here's how it works in practice:

1. Window Definition: A fixed time duration is established, let's say 60 seconds. This means windows start at t=0, t=60, t=120, and so on.
2. Request Arrival: When a request arrives from a client, the system first determines which fixed window it falls into based on the current timestamp.
3. Counter Increment: A counter associated with that client and that specific window is incremented.
4. Limit Check: After incrementing, the counter's value is compared against a predefined maximum limit.
   • If the counter is less than or equal to the limit, the request is allowed to proceed.
   • If the counter exceeds the limit, the request is denied (typically with an HTTP 429 Too Many Requests status code) until the current window ends and a new one begins.
5. Window Reset: Crucially, when a new window begins, the counter for the previous window is discarded or automatically reset, effectively starting fresh for the new interval. This reset happens instantaneously at the window boundary.

For example, if the limit is 100 requests per 60-second window for a given user, all requests from that user between 00:00:00 and 00:00:59 would increment a counter for the "00:00:00 window." Once 00:01:00 hits, a new counter for the "00:01:00 window" starts from zero, regardless of how many requests were made in the previous window.

Advantages of the Fixed Window Algorithm

The simplicity of the Fixed Window algorithm translates into several tangible benefits:

• Ease of Implementation: From a developer's perspective, this algorithm is remarkably straightforward to code. It primarily involves maintaining a counter and a timer, which can be easily managed by in-memory key-value stores like Redis. The logic is clear and requires minimal computational overhead.
• Low Resource Overhead: Unlike algorithms that track individual request timestamps (like Sliding Window Log), Fixed Window only needs to store a single counter and its expiry time per client per window. This makes it very memory-efficient, especially when dealing with a large number of clients or high request volumes.
• Predictable Behavior: The limits are clearly defined for fixed time segments, making it easy for both the service provider and the API consumers to understand when limits will reset. This predictability aids in debugging and communication.
• Atomic Operations Friendly: The core operations—incrementing a counter and setting an expiry—are atomic in many modern data stores, including Redis, ensuring data consistency even under high concurrency. This eliminates complex locking mechanisms often required in other distributed systems.

Disadvantages and the "Burst Problem"

Despite its advantages, the Fixed Window algorithm is not without its drawbacks, the most prominent of which is the "burst problem" or "edge case problem."

Consider our example of 100 requests per 60-second window.

• A user could make 100 requests at 00:00:59 (the very end of the first window).
• Immediately after, at 00:01:00 (the very beginning of the next window), they could make another 100 requests.

In this scenario, the user has effectively made 200 requests within a span of just two seconds (from 00:00:59 to 00:01:00), far exceeding the intended rate limit of 100 requests per minute. This concentrated burst of requests can still overwhelm backend services, despite the rate limit being technically enforced across the fixed windows. The "double-dipping" effect at the window boundary is a critical limitation to consider.

Other potential disadvantages include:

• Uneven Traffic Distribution: While it limits the total requests within a window, it doesn't smooth out traffic. All requests could still come in a sudden burst at the start of a window, leading to temporary spikes in load.
• Lack of Graceful Degradation: When a limit is hit, all subsequent requests within that window are immediately rejected. There's no mechanism for allowing a few extra requests or throttling them more gently.

Ideal Use Cases for Fixed Window

Given its characteristics, Fixed Window rate limiting is particularly well-suited for:

• Simple API Protection: For APIs where occasional short bursts of traffic are acceptable and the primary goal is to prevent sustained abuse or extreme overconsumption.
• Non-Critical Endpoints: APIs where the impact of the "burst problem" is minimal, such as retrieving static content, public data, or non-transactional lookups.
• Internal Service-to-Service Communication: In controlled environments where client behavior is generally trusted, and basic limits are needed to catch errant services.
• Secondary Rate Limits: Sometimes used in conjunction with other algorithms as a first line of defense or to enforce a stricter, broader limit while more granular limits are handled elsewhere.

While the "burst problem" is a notable concern, for many applications, the benefits of simplicity and efficiency offered by Fixed Window rate limiting outweigh this limitation, especially when implemented effectively with a high-performance data store like Redis. Its ease of integration, particularly within an api gateway, makes it an attractive starting point for comprehensive traffic management strategies.

Why Redis is the Undisputed Champion for Rate Limiting

When it comes to implementing high-performance, distributed rate limiting, Redis stands head and shoulders above many other solutions. Its unique architecture and feature set make it an ideal choice for the demanding requirements of traffic control in modern microservices and api ecosystems. Let's explore the key attributes that cement Redis's position as the go-to data store for rate limiting.

1. Blazing Fast In-Memory Data Store

Redis is fundamentally an in-memory data store, which is its most significant advantage for real-time operations like rate limiting. All data resides primarily in RAM, eliminating the latency associated with disk I/O that traditional databases incur. This means that checking and updating a rate limit counter involves minimal overhead, often completing in microseconds. In a system processing thousands or even millions of requests per second, this speed is absolutely critical. Any noticeable delay in the rate limiting check would directly impact the overall latency of every API call, severely degrading user experience. Redis's ability to respond almost instantaneously ensures that rate limiting enforcement doesn't become a bottleneck itself.

2. Atomic Operations for Consistency

The heart of rate limiting logic often revolves around incrementing a counter and checking its value. In a highly concurrent environment, where multiple application instances might try to update the same counter simultaneously, race conditions are a major concern. If these operations are not atomic (indivisible), requests could be mistakenly allowed or denied.

Redis provides atomic operations out-of-the-box, such as INCR (increment a key's value), INCRBY (increment by a specified amount), and EXPIRE (set a timeout on a key). These commands are executed as a single, indivisible operation on the server, guaranteeing that:

• When multiple clients try to INCR the same key concurrently, each increment will be applied exactly once, and the final value will be correct.
• Setting an expiry on a key immediately after incrementing it is also done atomically, preventing scenarios where a counter is incremented but fails to expire, leading to permanent blocking.

This atomic guarantee simplifies the rate limiting logic significantly, removing the need for complex distributed locks and ensuring reliable, consistent enforcement across all instances of an application.

3. Native Support for Time-to-Live (TTL)

Fixed Window rate limiting inherently relies on counters that expire at the end of each window. Redis's EXPIRE command is perfectly suited for this. When a counter is initialized or incremented, an EXPIRE command can be associated with it, setting a specific duration after which Redis will automatically delete the key.

This native TTL support:

• Simplifies Logic: Developers don't need to implement their own garbage collection mechanisms for expired counters. Redis handles it efficiently in the background.
• Saves Memory: Expired keys are automatically purged, preventing the accumulation of stale rate limit data and ensuring that memory usage remains optimized.
• Ensures Correctness: The automatic expiry guarantees that counters are reset precisely at the window boundaries, adhering to the Fixed Window algorithm's design.

4. Scalability and High Availability

Modern applications demand high scalability and fault tolerance. Redis is designed to meet these needs:

• Clustering: Redis Cluster allows distributing data across multiple Redis nodes, enabling horizontal scaling to handle massive amounts of data and throughput. For rate limiting, this means handling millions of distinct clients and billions of requests without a single point of failure. The cluster transparently shards keys, ensuring that rate limit counters are distributed and accessible.
• Replication and Sentinel: Redis supports master-replica replication for high availability. If a master node fails, Redis Sentinel (a distributed system) can automatically detect the failure and promote a replica to master, ensuring continuous service. This is critical for rate limiting, as any downtime could lead to unthrottled traffic and potential system overload.

These features ensure that the rate limiting infrastructure itself is robust and can scale with the demands of the application, a crucial aspect for any production-grade api gateway or service.

5. Versatility Beyond Rate Limiting

While Redis excels at rate limiting, its utility extends far beyond this single use case. It serves as a versatile Swiss Army knife for various application needs:

• Caching: Redis is a premier caching solution, significantly reducing database load and improving response times.
• Session Management: Storing user session data for fast retrieval.
• Message Broker: Implementing publish/subscribe patterns for real-time communication.
• Leaderboards and Analytics: Its sorted sets are perfect for real-time leaderboards.
• Queues: Simple message queues for background processing.

This versatility means that integrating Redis for rate limiting can also open doors for leveraging it for other critical functionalities, consolidating infrastructure and simplifying the technology stack. Many api gateway solutions leverage Redis for a variety of their internal mechanisms, including caching and authentication, making its adoption for rate limiting a natural extension.

6. Simplicity of Data Model

Redis's key-value store model is inherently simple. For Fixed Window rate limiting, a single string key (e.g., rate_limit:user123:api_endpoint:1678886400) storing an integer counter is all that's typically needed. This straightforward data model contributes to its speed and ease of use, reducing cognitive load for developers and simplifying debugging.

In summary, Redis provides the perfect blend of speed, atomic guarantees, native TTL, scalability, and versatility, making it the premier choice for implementing Fixed Window rate limiting. Its capabilities directly address the core requirements for building a robust and efficient traffic control system, a critical component for protecting any api or service in a distributed environment.

Implementing Fixed Window Rate Limiting with Redis: A Practical Guide

Now that we understand the "why" behind using Redis, let's dive into the "how." Implementing Fixed Window rate limiting with Redis is remarkably straightforward due to Redis's intuitive command set. This section will walk you through the core concepts and provide practical implementation steps, including pseudocode examples.

Core Concepts: Keys, Values, and EXPIRE

The implementation hinges on three fundamental Redis elements:

1. Keys: Each rate limit counter needs a unique key. This key typically encodes information about the entity being limited (e.g., user ID, IP address, API key), the specific resource being accessed (e.g., endpoint), and crucially, the current time window.
   • Example Key Structure: rate_limit:{identifier}:{resource}:{window_timestamp}
     • identifier: Could be user:123, ip:192.168.1.1, apikey:abcXYZ.
     • resource: Could be login, search, upload_photo.
     • window_timestamp: The start time of the current fixed window, typically in Unix epoch seconds, truncated to the window size (e.g., if window is 60 seconds, floor(current_time_in_seconds / 60) * 60).
2. Values: The value associated with each key is a simple integer, representing the number of requests made within that specific window by that specific identifier for that resource.
3. EXPIRE Command: This is vital for automatically resetting windows. When a counter key is first created or incremented, an EXPIRE command is set on it, typically for the duration of the window. For instance, if the window is 60 seconds, the key would expire after 60 seconds.

Basic Implementation Steps (Pseudocode)

Let's assume a rate limit of max_requests per window_size_seconds for a given identifier accessing a resource.

FUNCTION check_rate_limit(identifier, resource, max_requests, window_size_seconds):
    # 1. Determine the current fixed window timestamp
    current_time_in_seconds = GET_CURRENT_UNIX_TIMESTAMP()
    window_start_timestamp = FLOOR(current_time_in_seconds / window_size_seconds) * window_size_seconds

    # 2. Construct the Redis key for this window
    redis_key = "rate_limit:" + identifier + ":" + resource + ":" + window_start_timestamp

    # 3. Increment the counter and get its new value atomically
    #    If the key doesn't exist, INCR initializes it to 0 before incrementing to 1.
    current_request_count = REDIS.INCR(redis_key)

    # 4. Set the expiry for the key if it's new (or ensure it exists)
    #    The EXPIRE command only sets expiry if it's not already set, or if the key was just created.
    #    Alternatively, EXPIRE can be called unconditionally. It's safe to call multiple times.
    #    The expiry should be (window_start_timestamp + window_size_seconds) - current_time_in_seconds
    #    This means the key will expire exactly when the window ends, relative to its creation.
    IF current_request_count == 1:
        # This is the first request in this window, so set the expiry
        REDIS.EXPIRE(redis_key, window_size_seconds)
        # Note: A subtle point here is that EXPIRE should ideally be set relative to the
        # actual start of the window, not just `window_size_seconds` from the first request's arrival.
        # A more robust approach, especially in distributed systems, might be to calculate the
        # remaining time until the NEXT window starts:
        # time_to_expire = (window_start_timestamp + window_size_seconds) - current_time_in_seconds
        # This guarantees the key expires exactly at the end of the window.
        time_to_expire = (window_start_timestamp + window_size_seconds) - current_time_in_seconds
        IF time_to_expire > 0:
            REDIS.EXPIRE(redis_key, time_to_expire)
        ELSE:
            # Handle edge case where current_time_in_seconds is exactly at the window boundary
            # or slightly past, though the floor operation should prevent this from being negative.
            # If time_to_expire <= 0, it means the window has just ended or is about to.
            # It's safer to ensure expiry is at least 1 second.
            REDIS.EXPIRE(redis_key, window_size_seconds) # Fallback, or handle logic for new window directly

    # 5. Check if the request count exceeds the limit
    IF current_request_count > max_requests:
        RETURN false # Limit exceeded
    ELSE:
        RETURN true # Request allowed
END FUNCTION

Considerations for Different Identifiers

The identifier in the Redis key is crucial for granular rate limiting:

• IP Address:
  • Pros: Easy to obtain, no user authentication required.
  • Cons: Users behind NAT (e.g., corporate networks, mobile carriers) share an IP and can collectively hit limits. Malicious actors can spoof IPs (though harder at the network layer) or use proxy networks.
  • Key Example: rate_limit:ip:192.168.1.100:search:1678886400
• User ID:
  • Pros: Most accurate for individual user limits, persists across devices/IPs.
  • Cons: Requires user authentication, so unauthenticated requests (e.g., login API itself) can't use this.
  • Key Example: rate_limit:user:uuid-12345:profile_update:1678886400
• API Key/Client ID:
  • Pros: Ideal for third-party api consumers, allows specific limits for different integrations.
  • Cons: Requires an API key management system.
  • Key Example: rate_limit:apikey:client-app-xyz:data_fetch:1678886400
• Combination: For comprehensive protection, a multi-layered approach is often best. For instance, apply a strict IP-based limit for unauthenticated traffic and a more generous user ID or API key-based limit for authenticated requests. An api gateway is exceptionally well-suited to handle these multi-layered policies, applying different rate limits based on headers, authentication status, or routing rules.

Atomicity and Lua Scripts for Enhanced Reliability

The pseudocode above performs two distinct Redis commands (INCR and EXPIRE). While INCR is atomic, and EXPIRE is also atomic for a single key, a slight race condition could theoretically exist if a server crashes between INCR and EXPIRE, leading to a counter that never expires. This is a very rare edge case, but for absolute robustness, especially in a high-traffic, highly critical api, Redis Lua scripting provides a solution.

A Lua script allows multiple Redis commands to be executed as a single, atomic operation on the Redis server, guaranteeing that either all commands in the script succeed or none do.

-- Lua script for atomic fixed window rate limiting
-- KEYS[1] = redis_key (e.g., rate_limit:user:123:search:1678886400)
-- ARGV[1] = window_size_seconds
-- ARGV[2] = max_requests

local current_request_count = redis.call('INCR', KEYS[1])
local time_to_expire = ARGV[1]

-- If this is the first request in the window (count is 1), set the expiry
if current_request_count == 1 then
    redis.call('EXPIRE', KEYS[1], time_to_expire)
end

-- Return the current count
return current_request_count

How to use this Lua script:

1. Load the script into Redis once (e.g., using SCRIPT LOAD). Redis returns a SHA1 hash of the script.
2. Execute the script using EVALSHA with the SHA1 hash, providing the redis_key in KEYS[1] and window_size_seconds and max_requests in ARGV.

The application logic then becomes:

FUNCTION check_rate_limit_with_lua(identifier, resource, max_requests, window_size_seconds):
    current_time_in_seconds = GET_CURRENT_UNIX_TIMESTAMP()
    window_start_timestamp = FLOOR(current_time_in_seconds / window_size_seconds) * window_size_seconds
    redis_key = "rate_limit:" + identifier + ":" + resource + ":" + window_start_timestamp

    -- Calculate the exact time to expire relative to the window boundary
    time_to_expire_seconds = (window_start_timestamp + window_size_seconds) - current_time_in_seconds
    IF time_to_expire_seconds <= 0:
        time_to_expire_seconds = window_size_seconds -- Fallback if we're exactly at or past boundary

    -- Execute the Lua script
    actual_count = REDIS.EVAL_LUA_SCRIPT_BY_SHA(
        "sha1_of_script",
        1,                     -- Number of KEYS
        redis_key,             -- KEYS[1]
        time_to_expire_seconds, -- ARGV[1]
        max_requests           -- ARGV[2] (passed to script but not strictly used for check inside Lua)
    )

    IF actual_count > max_requests:
        RETURN false
    ELSE:
        RETURN true
END FUNCTION

By leveraging Redis's atomic operations, either directly with INCR and EXPIRE or more robustly with Lua scripts, developers can build a highly reliable and performant Fixed Window rate limiting system. This foundation is essential for protecting any modern api or service from unintended abuse and ensuring stable operation.

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Addressing Challenges and Best Practices in Fixed Window Rate Limiting

While Fixed Window rate limiting with Redis offers a powerful and efficient solution, real-world deployment comes with its share of challenges. Implementing it effectively requires not just understanding the mechanism but also anticipating potential issues and adopting best practices.

1. Mitigating the Burst Problem

As discussed, the primary weakness of Fixed Window is the "burst problem" at window edges. While it's an inherent limitation, there are strategies to mitigate its impact:

• Graceful Degradation: Instead of hard-rejecting all requests once the limit is hit, consider allowing a small percentage of requests to pass through, but with significantly increased latency or reduced functionality. This can provide a better user experience for slightly over-limit users while still applying pressure.
• Client-Side Throttling and Backoff: Encourage or enforce clients to implement exponential backoff and retry mechanisms when they receive a 429 Too Many Requests status. This shifts some of the responsibility to the client, preventing them from hammering the api unnecessarily.
• Layering with Other Algorithms (Hybrid Approach): For critical endpoints where bursts are unacceptable, combine Fixed Window with another algorithm. For example, a global Fixed Window limit can be supplemented with a very small Token Bucket or Leaky Bucket on a per-second basis. This allows for a burst over a minute but caps the requests within any given second. An api gateway can be configured to apply multiple rate limiting policies simultaneously, enabling such hybrid strategies.
• Smaller Windows for Critical APIs: If a 60-second window is too susceptible to bursts, consider using smaller windows (e.g., 10-second or 5-second windows) with proportionally smaller limits. This makes bursts less severe, though it increases the number of Redis keys.

2. Choosing the Right Window Size and Limit

The selection of window_size_seconds and max_requests is a critical design decision that impacts both user experience and system performance.

• User Experience: Too strict limits can frustrate legitimate users, leading to abandoned applications. Too lenient limits can expose your system to abuse.
• Service Capacity: The limit should reflect the actual capacity of your backend services and databases. What can your system realistically handle per minute/hour without degradation?
• Business Logic: Different API endpoints may have different sensitivities. A login api might need a very tight limit (e.g., 5 requests per minute) to prevent brute-force attacks, while a public data retrieval api could have a much higher limit (e.g., 500 requests per minute).
• Monitoring and Iteration: Start with reasonable estimates, then closely monitor api usage and system performance. Adjust limits based on observed patterns, customer feedback, and incident reports.

3. Handling Distributed Systems and Redis Clustering

In a microservices architecture, multiple instances of your application might be running, all trying to access the same Redis instance or cluster.

• Redis Cluster Compatibility: Ensure your Redis client library is cluster-aware. It should handle key sharding transparently, directing requests to the correct Redis node that holds the rate limit key.
• Consistency: Redis's atomic operations largely handle consistency within the cluster for single-key operations. For operations spanning multiple keys (which Fixed Window typically doesn't, unless you're aggregating limits), Lua scripts or Redis transactions (MULTI/EXEC) are essential.
• Network Latency: Even with Redis's speed, network latency between your application instances and the Redis server can add up. Locate your Redis cluster geographically close to your application servers.
• Connection Pooling: Use connection pooling in your application to efficiently manage connections to Redis, reducing the overhead of establishing new connections for every request.

4. Robust Monitoring and Alerting

A rate limiting system is only as good as its ability to inform you of issues.

• Key Metrics to Collect:
  • Rate Limit Hits: How many requests are being denied due to rate limits? Track this per identifier, resource, and overall.
  • Requests Allowed: The number of requests successfully processed.
  • Traffic Patterns: Observe request volume over time to identify unusual spikes or sustained high usage.
  • Redis Metrics: Monitor Redis CPU usage, memory consumption, number of connections, latency, and hit/miss ratio to ensure Redis itself is performing optimally.
• Alerting: Set up alerts for:
  • High rates of 429 responses (potential attack or misbehaving client).
  • Sudden drops in allowed requests (potential misconfiguration or widespread client issues).
  • Redis performance degradation (high latency, OOM errors).
• Logging: Detailed logs of rate limit decisions (allowed/denied) are invaluable for debugging and security audits. Include the identifier, resource, and the current count when a limit is hit.

5. Security Implications and Response to Breaches

Rate limiting is a security measure; treat it as such.

• Preventing Bypass: Ensure that your rate limiting logic is applied as early as possible in the request lifecycle, ideally at the gateway or load balancer, before requests consume significant backend resources. Do not trust client-side headers for identification; always verify identifiers on the server side.
• Responding to 429: When a request is denied, return an HTTP 429 Too Many Requests status code. Include appropriate headers:
  • Retry-After: Suggests how long the client should wait before retrying (e.g., Retry-After: 60 for 60 seconds).
  • X-RateLimit-Limit: The total requests allowed in the window.
  • X-RateLimit-Remaining: The number of requests remaining in the current window.
  • X-RateLimit-Reset: The Unix timestamp when the current window resets. These headers help legitimate clients implement backoff strategies and understand their limits.
• Beyond Blocking: For severe or persistent abuse, merely blocking requests might not be enough. Integrate rate limit breach alerts with broader security systems that can trigger:
  • Temporary IP blocking at the firewall level.
  • Flagging user accounts for review.
  • CAPTCHA challenges.

By meticulously addressing these challenges and adhering to best practices, your Fixed Window Redis rate limiting implementation can evolve from a basic throttle to a robust and integral component of your service's resilience and security strategy. This proactive approach ensures a stable, fair, and secure environment for all api consumers.

The Indispensable Role of an API Gateway in Rate Limiting

While implementing rate limiting directly within application code is feasible, a dedicated api gateway emerges as the quintessential orchestrator for such policies in a microservices or large-scale api ecosystem. An api gateway acts as a single entry point for all client requests, routing them to the appropriate backend services while simultaneously handling a myriad of cross-cutting concerns, with rate limiting being one of the most critical.

Centralized Enforcement: The First Line of Defense

An api gateway provides a centralized control plane where rate limiting policies can be defined and enforced uniformly across all inbound traffic. Instead of scattering rate limiting logic across dozens or hundreds of microservices, each potentially implementing it differently, the gateway ensures consistency and eliminates redundancy.

• Before the Backend: The gateway intercepts every request before it reaches any of your valuable backend services. This means that denied requests consume minimal backend resources, protecting your services from even being touched by excessive traffic. This is a fundamental advantage, as it offloads the burden of traffic management from your application logic, allowing microservices to focus solely on their core business functions.
• Unified Policy: Whether it's an api for user authentication, data retrieval, or complex financial transactions, the gateway can apply a consistent rate limiting scheme or tailor policies based on specific endpoint requirements. This uniformity reduces configuration errors and simplifies audits.

Configuration and Flexibility at the Gateway Level

Modern api gateway solutions offer highly configurable rate limiting capabilities. Administrators can define limits based on a variety of criteria:

• Client Identifier: IP address, API key, JWT claims (user ID), custom headers.
• Request Attributes: HTTP method, path, query parameters, payload content.
• Service/Route: Apply different limits to different backend services or individual api endpoints.
• Tiered Limits: Implement different rate limits for various client tiers (e.g., free tier vs. premium tier).

This flexibility allows for fine-grained control, ensuring that rate limiting policies align perfectly with business requirements and service level agreements (SLAs). For instance, a public api might have a broad IP-based limit, while authenticated users accessing a premium feature might have a higher limit based on their user ID, all configured and enforced at the gateway.

Advantages of Gateway-Level Rate Limiting

The benefits of offloading rate limiting to an api gateway are substantial:

1. Reduced Load on Backend Services: By rejecting excessive requests at the edge, the gateway significantly reduces the computational burden on your application servers, allowing them to focus on processing legitimate traffic.
2. Consistent Policy Enforcement: Ensures that all services adhere to the same rate limiting rules, eliminating discrepancies and potential loopholes that could arise from disparate implementations.
3. Enhanced Visibility and Logging: Gateways often come with robust logging and monitoring capabilities, providing a clear picture of rate limit hits, traffic patterns, and potential abuse attempts across the entire api landscape. This centralized visibility is crucial for security audits and operational insights.
4. Simplified Development and Operations: Developers don't need to write rate limiting code in every service, simplifying application logic. Operations teams can manage and update rate limit policies without deploying new service versions.
5. Improved Security Posture: As the first line of defense, the gateway can apply more advanced security policies, including DDoS mitigation, authentication, and authorization, in conjunction with rate limiting.

Integrating Redis with an API Gateway

Many popular api gateway solutions, both open-source and commercial, are designed to integrate seamlessly with external data stores like Redis for their rate limiting mechanisms. This integration typically happens in one of two ways:

• Built-in Redis Connectors: The gateway might have native support for connecting to a Redis instance or cluster, using Redis commands (like INCR and EXPIRE) internally to manage rate limit counters.
• Extensible Plugin Architectures: Many gateways allow for custom plugins or middleware. Developers can write specific plugins that implement Fixed Window rate limiting (or other algorithms) using Redis as the backend store. This provides immense flexibility to tailor the rate limiting logic to precise requirements.

The gateway leverages Redis's speed and atomic operations to maintain and check counters efficiently, ensuring that rate limiting decisions are made with minimal latency, even under high traffic loads.

For robust api management and advanced gateway functionalities, platforms like APIPark offer comprehensive solutions. As an open-source AI gateway and api management platform, APIPark is designed to streamline the entire api lifecycle, from design and publication to invocation and decommission. It provides efficient rate limiting mechanisms as part of its end-to-end management capabilities, protecting your services while also facilitating quick integration of 100+ AI models and ensuring unified api formats. Leveraging APIPark allows developers to centralize not only traffic management policies like rate limiting but also authentication, cost tracking, and prompt encapsulation into new REST apis, helping businesses manage, integrate, and deploy AI and REST services with unparalleled ease and security.

By deploying rate limiting at the api gateway level, organizations ensure that their services are protected comprehensively and efficiently, transforming traffic management from a distributed headache into a streamlined, centralized, and robust defense mechanism for all their digital apis. This strategic placement not only enhances security and stability but also optimizes resource utilization and simplifies the overall operational overhead of managing complex microservices architectures.

Advanced Redis Techniques for Enhanced Rate Limiting

While the basic INCR and EXPIRE commands form the backbone of Fixed Window rate limiting, Redis offers more sophisticated features that can elevate the robustness and performance of your rate limiting implementation. These techniques move beyond the simple counter and allow for more atomic, efficient, and versatile solutions.

1. The Power of Lua Scripts for Atomicity

We briefly touched upon Lua scripts for ensuring atomicity of INCR and EXPIRE. Let's delve deeper into why this is a critical best practice, especially in highly concurrent or distributed environments.

The Problem with Separate Commands: Executing REDIS.INCR(key) and then REDIS.EXPIRE(key, ttl) as two separate commands, even if very fast, introduces a tiny window of vulnerability. If your application crashes or the network disconnects between these two commands, the key might be incremented but never receive its EXPIRE time. This would lead to a "sticky" counter that never resets, effectively permanently blocking future requests from that identifier. While rare, it's a non-trivial risk in production systems.

Lua Script as a Transaction: Redis executes Lua scripts atomically. This means the entire script runs as a single, uninterrupted operation on the Redis server. No other commands can interleave with the script's execution. Therefore, by encapsulating both INCR and EXPIRE within a Lua script, you guarantee that either both operations complete successfully or neither does.

Example Lua Script (More Elaborate):

-- KEYS[1]: The rate limit key (e.g., "rate_limit:user:123:search:1678886400")
-- ARGV[1]: The maximum number of requests allowed (limit)
-- ARGV[2]: The window size in seconds (ttl)

local count = redis.call('INCR', KEYS[1])

-- Only set the expiry if this is the first request in the window.
-- If the key already existed, its expiry is untouched or already present.
-- This ensures the expiry is set relative to the *first* request in the window,
-- making sure the window duration is consistent.
if count == 1 then
    redis.call('EXPIRE', KEYS[1], ARGV[2])
end

-- Return the current count and remaining time for client feedback
local ttl = redis.call('TTL', KEYS[1])
-- If ttl is -1 (no expire) or -2 (key doesn't exist anymore), convert to actual window time
if ttl == -1 or ttl == -2 then
    ttl = ARGV[2] -- Fallback to full window if no expiry set or expired
end

return {count, ttl}

This script does more than just increment and expire; it also fetches the TTL (Time To Live) for the key. This TTL can be very useful for informing clients via X-RateLimit-Reset headers in an api gateway, allowing them to implement proper backoff strategies. The client can then check if count > ARGV[1] to decide whether to allow the request.

2. Leveraging Redis Data Structures for Complexity

While Fixed Window primarily uses simple string keys, Redis's rich data structures can be explored for more complex or hybrid rate limiting scenarios.

• Redis Hashes for Multiple Limits/Metrics: If you need to store multiple pieces of information per user/resource within a window (e.g., current count, last request timestamp, number of errors), a Redis Hash (HSET, HGET, HINCRBY) can be very efficient.
  • Key: rate_limit_hash:{identifier}:{window_timestamp}
  • Hash Fields: count, last_access, error_count. This can be useful for diagnostics or for dynamic adjustments to limits based on recent behavior.
• Redis Sorted Sets for Sliding Window (Brief Mention): Although outside the scope of Fixed Window, it's worth noting that Redis Sorted Sets are the go-to data structure for implementing more accurate Sliding Window Log algorithms. Each request's timestamp can be stored as a member in a sorted set, with the score also being the timestamp. ZCOUNT and ZREMRANGEBYSCORE can then efficiently count and prune old requests. This highlights Redis's versatility beyond simple counters.

3. Performance Considerations Beyond Basic Commands

Optimizing Redis usage for rate limiting involves more than just selecting the right commands; it also considers network interactions and Redis server load.

• Connection Pooling: Re-establishing a TCP connection for every Redis command is inefficient. Use a robust Redis client library that implements connection pooling. This maintains a pool of open, ready-to-use connections, significantly reducing connection overhead and improving throughput.
• Pipelining (MULTI/EXEC or Client-Side Pipelining): If you have multiple Redis commands that need to be executed sequentially without dependencies, Redis Pipelining can drastically improve performance. Instead of sending one command, waiting for a response, sending the next, etc., pipelining sends multiple commands in a single network round trip and then reads all responses at once. While Lua scripts achieve atomicity and implicitly pipeline operations, explicit pipelining can be useful for fetching multiple rate limits concurrently if an api call triggers checks against several distinct limits (e.g., global limit, user-specific limit, endpoint-specific limit). python # Example Python Pipelining (Conceptual) pipe = redis_client.pipeline() pipe.incr("key1") pipe.expire("key1", 60) pipe.incr("key2") pipe.expire("key2", 300) results = pipe.execute()
• Client-Side Caching for Low-Frequency Limits: For very generous rate limits (e.g., 100,000 requests per day), it might be overkill to hit Redis on every single request. A small, short-lived client-side cache (e.g., an in-memory map in your application instance) could store counts and only write back to Redis periodically or after a certain threshold. This introduces some eventual consistency and potentially allows slight over-limits but can significantly reduce Redis load for non-critical, high-volume limits. This approach needs careful consideration of trade-offs.

By incorporating these advanced techniques, particularly Lua scripting for atomic operations and intelligent connection management, you can build a Redis-backed Fixed Window rate limiting system that is not only robust and correct but also performs exceptionally well under high load, forming a critical component of any resilient api infrastructure.

Deployment and Scaling Considerations for Redis Rate Limiting

A perfectly designed rate limiting algorithm is only as effective as its deployment. For high-traffic applications, simply running a single Redis instance isn't enough. Thoughtful planning for deployment, scalability, and high availability (HA) is paramount to ensure your rate limiting infrastructure can withstand real-world demands.

1. Single Instance vs. Redis Cluster

The choice between a single Redis instance and a Redis Cluster depends heavily on your application's scale and requirements.

• Single Instance:
  • Pros: Simplest to set up and manage, suitable for small to medium-sized applications, or scenarios where rate limits don't see extreme concurrency.
  • Cons: A single point of failure (unless configured with replication and Sentinel), limited by the resources of a single server (CPU, RAM, network bandwidth). Cannot scale horizontally beyond a certain point.
  • Use Case: Development environments, smaller microservices with moderate traffic, internal apis with fewer concurrent users.
• Redis Cluster:
  • Pros: Designed for horizontal scaling, distributes data across multiple nodes (sharding), handles massive amounts of data and throughput. Provides automatic data sharding, rebalancing, and failover (if configured correctly). Eliminates single points of failure across the data store.
  • Cons: More complex to set up, manage, and monitor than a single instance. Requires a minimum of 3 master nodes for basic high availability.
  • Use Case: Large-scale public apis, high-traffic gateway services, distributed microservices architectures with millions of unique identifiers and requests per second. For applications where a comprehensive api gateway solution like APIPark handles vast amounts of traffic, a Redis Cluster is almost certainly the required backend for rate limiting.

2. High Availability (HA) with Replication and Sentinel

Even if you start with a single instance, implementing high availability is crucial for any production system.

• Master-Replica Replication: A master Redis instance handles all write operations, and one or more replica instances asynchronously receive copies of the data. If the master fails, a replica can be promoted.
• Redis Sentinel: This is Redis's recommended HA solution for non-clustered setups. Sentinel is a distributed system that monitors Redis master and replica instances. If a master fails, Sentinels can automatically:
  1. Detect the failure.
  2. Promote a replica to become the new master.
  3. Reconfigure other replicas to follow the new master.
  4. Inform applications about the new master's address. This automated failover is vital for rate limiting, as prolonged downtime could leave your apis vulnerable to abuse.

3. Persistence: RDB vs. AOF

Redis primarily operates in memory, but it offers persistence options to prevent data loss upon restarts or failures.

• RDB (Redis Database) Snapshots:
  • Mechanism: Periodically saves a point-in-time snapshot of the dataset to disk in a compact binary format.
  • Pros: Very fast for backups/restores, good for disaster recovery. Minimal performance impact during save.
  • Cons: If Redis crashes between snapshots, you might lose the most recent data (e.g., rate limit counts from the last few minutes).
• AOF (Append Only File):
  • Mechanism: Logs every write operation received by the server. This file can be replayed to reconstruct the dataset.
  • Pros: Offers better durability (can be configured to fsync every write or every second), minimizing data loss.
  • Cons: AOF files can grow large. Higher performance overhead for writes compared to RDB.
• Recommendation for Rate Limiting: For rate limiting, strict durability is often less critical than for core application data. Rate limit counters are temporary by nature due to TTLs. Losing a few seconds or minutes of counter data usually means a brief window where limits might be slightly off, which is often acceptable. Therefore, a balance is needed:
  • RDB: Often sufficient, especially with a reasonable snapshot frequency. It provides good recovery without significant write performance impact.
  • AOF with fsync=everysec: Provides higher durability if absolutely minimal data loss is required, but with a slight performance trade-off. Given the temporary nature of fixed window counters, a complete loss of the rate limit state upon a catastrophic Redis failure usually results in a temporary period of unthrottled traffic until the system recovers and new windows begin, rather than permanent data corruption.

4. Memory Management and Eviction Policies

As an in-memory data store, memory management is crucial.

• Maxmemory Directive: Configure maxmemory in your Redis configuration to set an upper limit on memory usage.
• Eviction Policy: When maxmemory is reached, Redis needs to decide which keys to evict to free up space. For rate limiting, the allkeys-lru (Least Recently Used) or volatile-lru (LRU among keys with an expire set) policies are often suitable. Keys that are no longer actively accessed (i.e., their window has passed or they haven't been incremented in a while) will be evicted first. Since rate limit keys have TTLs, volatile-lru is often a good choice, ensuring keys without explicit expires are preserved, while expiring keys are prioritized for eviction if they are old and inactive.
• Monitoring Memory Usage: Regularly monitor used_memory and used_memory_rss to ensure Redis isn't hitting its limits or swapping to disk (which severely degrades performance).

5. Robust Monitoring of Redis Itself

Just as monitoring your application is vital, so is monitoring your Redis instances.

• Key Redis Metrics to Track:
  • Latency: INFO commandstats can show the average execution time of commands. High latency indicates an overloaded server.
  • Memory Usage: Track used_memory and used_memory_rss.
  • CPU Usage: used_cpu_sys and used_cpu_user metrics.
  • Connections: connected_clients can indicate potential connection leaks or unusually high demand.
  • Hit/Miss Ratio: keyspace_hits vs. keyspace_misses gives insight into cache effectiveness (though less critical for rate limiting counters, as they are mostly writes/reads of existing keys).
  • Persistence Status: Ensure RDB/AOF saves are completing successfully.
  • Replication Lag: For master-replica setups, monitor master_repl_offset on replicas to detect lag.
  • Evictions: evicted_keys metric indicates how often Redis is forced to evict keys to free memory.
• Tools: Use Redis's INFO command, redis-cli --latency, and integrate with external monitoring systems (Prometheus, Grafana, Datadog) to visualize trends and set up alerts.

By carefully considering these deployment and scaling aspects, you can build a Redis-backed rate limiting solution that is not only highly performant but also resilient, scalable, and capable of protecting your apis under even the most demanding traffic conditions. This foundational robustness is essential for any enterprise-grade api gateway or microservices platform.

Conclusion: The Enduring Power of Fixed Window Rate Limiting with Redis

In the dynamic and often tumultuous landscape of modern digital services, the ability to control and manage the flow of requests is not merely a feature, but a fundamental necessity. Rate limiting stands as a critical guardian, ensuring stability, fairness, and security for every api and application. Among the various strategies, the Fixed Window algorithm offers an elegant balance of simplicity and effectiveness, making it an excellent choice for a wide array of use cases.

When coupled with Redis, the Fixed Window algorithm transforms into a robust and high-performance solution. Redis’s in-memory speed ensures real-time decision-making, its atomic operations guarantee data consistency even under intense concurrency, and its native time-to-live (TTL) support simplifies the window reset mechanism. Furthermore, Redis's inherent scalability, through features like clustering and replication, ensures that your rate limiting infrastructure can grow seamlessly with the demands of your application, from a handful of users to millions.

We've explored the implementation details, from basic INCR/EXPIRE commands to the enhanced reliability of Lua scripting, ensuring atomic execution of multiple operations. We also delved into crucial best practices, such as mitigating the "burst problem," intelligently choosing window sizes, and establishing comprehensive monitoring and alerting systems. Each of these elements contributes to building a resilient rate limiting system that effectively safeguards your services against abuse, ensures fair resource allocation, and maintains a high quality of service for your legitimate users.

Crucially, the role of an api gateway cannot be overstated in this entire orchestration. As the first line of defense, a gateway centralizes rate limiting enforcement, offloading traffic management from your backend services and providing a consistent, auditable policy across your entire api landscape. Solutions like APIPark exemplify how an advanced api gateway can integrate powerful rate limiting with comprehensive api lifecycle management, providing a unified platform for not just traffic control, but also AI model integration, security, and developer experience.

As technology continues to evolve, the need for intelligent traffic management will only intensify. Mastering Fixed Window Redis implementation for rate limiting equips developers and architects with a fundamental tool to build more robust, secure, and performant systems. It's an investment in the long-term health and reliability of your digital infrastructure, ensuring that your services can thrive even amidst the heaviest digital storms.

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Frequently Asked Questions (FAQ)

1. What is Fixed Window Rate Limiting and how does it work with Redis?

Fixed Window Rate Limiting divides time into distinct, non-overlapping windows (e.g., 60 seconds). For each window, a counter tracks requests from a specific client. Redis's INCR command is used to increment this counter atomically, and EXPIRE is used to set a time-to-live (TTL) on the counter key, automatically resetting it when the window ends. If the counter exceeds a predefined limit within its window, subsequent requests are denied until the next window begins.

2. What are the main advantages of using Redis for Fixed Window Rate Limiting?

Redis offers several key advantages: its in-memory nature provides extremely low-latency operations, crucial for real-time checks; its atomic commands (INCR, EXPIRE) guarantee consistency in concurrent environments; it has native TTL support to manage window resets automatically; and it is highly scalable (via Redis Cluster) and provides high availability (via Sentinel and replication).

3. What is the "burst problem" in Fixed Window Rate Limiting and how can it be mitigated?

The "burst problem" occurs when a client makes a high volume of requests at the very end of one window and immediately another high volume at the very beginning of the next window. This can result in twice the allowed requests within a short period across the window boundary, potentially overwhelming backend services. Mitigation strategies include implementing client-side throttling with exponential backoff, combining Fixed Window with other algorithms (hybrid approach), or using an API gateway to apply multi-layered rate limits.

4. How does an API Gateway enhance Fixed Window Rate Limiting?

An api gateway centralizes rate limiting enforcement, acting as the first line of defense for all incoming traffic. This offloads the burden from backend services, ensures consistent policy application across all APIs, provides better visibility and logging, and simplifies development. Many gateways, like APIPark, integrate with Redis to efficiently manage rate limit counters and apply flexible policies based on various criteria (IP, user ID, API key, etc.).

5. Is it safe to use INCR and EXPIRE as separate commands in Redis for rate limiting, or should I use Lua scripts?

While INCR and EXPIRE are individually atomic, executing them as separate commands introduces a small race condition: if your application crashes between the INCR and EXPIRE calls, the counter might be incremented but never expire, leading to permanent blocking. For absolute robustness and atomicity, especially in high-concurrency environments, it is strongly recommended to encapsulate both operations within a Redis Lua script. Lua scripts execute as a single, atomic transaction on the Redis server, guaranteeing that both operations succeed or fail together.

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