{
  "schema_version": 2,
  "id": "develop/use-cases/rate-limiter/java-jedis",
  "title": "Token bucket rate limiter with Redis and Java",
  "url": "https://redis.io/docs/latest/develop/use-cases/rate-limiter/java-jedis/",
  "summary": "Implement a token bucket rate limiter using Redis and Lua scripts in Java",
  "aliases": [
    "/develop/use-cases/rate-limiter/java",
    "/develop/use-cases/rate-limiter/jedis"
  ],
  "tags": [
    "docs",
    "develop",
    "stack",
    "oss",
    "rs",
    "rc"
  ],
  "last_updated": "2026-09-29T14:34:47+01:00",
  "children": [],
  "page_type": "content",
  "content_hash": "1b719b840ef317447074b60a88972fea7d4913405ca9bcc7398711f7ddf877e5",
  "sections": [
    {
      "id": "overview-1",
      "title": "Overview",
      "role": "overview",
      "text": "This guide shows you how to implement a distributed token bucket rate limiter using Redis and Lua scripts in Java with the [`Jedis`](https://redis.io/docs/latest/develop/clients/jedis) client library."
    },
    {
      "id": "overview",
      "title": "Overview",
      "role": "overview",
      "text": "Rate limiting is a critical technique for controlling the rate at which operations are performed. Common use cases include:\n\n* Limiting API requests per user or IP address\n* Preventing abuse and protecting against denial-of-service attacks\n* Ensuring fair resource allocation across multiple clients\n* Throttling background jobs or batch operations\n\nThe **token bucket algorithm** is a popular rate limiting approach that allows bursts of traffic while maintaining an average rate limit over time. This guide covers the Java implementation using the [`Jedis`](https://redis.io/docs/latest/develop/clients/jedis) client library, taking advantage of Java's `try-with-resources` for connection management, `JedisPool` for connection pooling, and checked exceptions for error handling."
    },
    {
      "id": "how-it-works",
      "title": "How it works",
      "role": "content",
      "text": "The token bucket algorithm works like a bucket that holds tokens:\n\n1. **Initialization**: The bucket starts with a maximum capacity of tokens\n2. **Refill**: Tokens are added to the bucket at a constant rate (for example, 1 token per second)\n3. **Consumption**: Each request consumes one token from the bucket\n4. **Decision**: If tokens are available, the request is allowed; otherwise, it's denied\n5. **Capacity limit**: The bucket never exceeds its maximum capacity\n\nThis approach allows for burst traffic (using accumulated tokens) while enforcing an average rate limit over time."
    },
    {
      "id": "why-use-redis",
      "title": "Why use Redis?",
      "role": "content",
      "text": "Redis is ideal for distributed rate limiting because:\n\n* **Atomic operations**: Lua scripts execute atomically, preventing race conditions\n* **Shared state**: Multiple application servers can share the same rate limit counters\n* **High performance**: In-memory operations provide microsecond latency\n* **Automatic expiration**: Keys can be set to expire automatically (though not used in this implementation)"
    },
    {
      "id": "the-lua-script",
      "title": "The Lua script",
      "role": "content",
      "text": "The core of this implementation is a Lua script that runs atomically on the Redis server. This ensures that checking and updating the token bucket happens in a single operation, preventing race conditions in distributed environments.\n\nHere's how the script works:\n\n[code example]"
    },
    {
      "id": "script-breakdown",
      "title": "Script breakdown",
      "role": "content",
      "text": "1. **State retrieval**: Uses [`HMGET`](https://redis.io/docs/latest/commands/hmget) to fetch the current token count and last refill time from a hash\n2. **Initialization**: On first use, sets tokens to full capacity\n3. **Token refill calculation**: Computes how many tokens should be added based on elapsed time\n4. **Capacity enforcement**: Uses `math.min()` to ensure tokens never exceed capacity\n5. **Token consumption**: Decrements the token count if available\n6. **State update**: Uses [`HMSET`](https://redis.io/docs/latest/commands/hmset) to save the new state\n7. **Return value**: Returns both the decision (allowed/denied) and remaining tokens"
    },
    {
      "id": "why-atomicity-matters",
      "title": "Why atomicity matters",
      "role": "content",
      "text": "Without atomic execution, race conditions could occur:\n\n* **Double spending**: Two requests could read the same token count and both succeed when only one should\n* **Lost updates**: Concurrent updates could overwrite each other's changes\n* **Inconsistent state**: Token count and refill time could become desynchronized\n\nUsing [`EVAL`](https://redis.io/docs/latest/commands/eval) or [`EVALSHA`](https://redis.io/docs/latest/commands/evalsha) ensures the entire operation executes atomically, making it safe for distributed systems."
    },
    {
      "id": "installation",
      "title": "Installation",
      "role": "setup",
      "text": "Add the Jedis dependency to your project:\n\n* If you use **Maven**:\n\n  [code example]\n\n* If you use **Gradle**:\n\n  [code example]"
    },
    {
      "id": "using-the-java-class",
      "title": "Using the Java class",
      "role": "content",
      "text": "The `TokenBucket` class provides a thread-safe interface for rate limiting\n([source](https://github.com/redis/docs/blob/main/content/develop/use-cases/rate-limiter/java-jedis/TokenBucket.java)):\n\n[code example]\n\nJedis operations are synchronous and thread-safe when using `JedisPool`, which manages a pool of connections internally. The `allow()` method returns a `RateLimitResult` record containing both the decision and the remaining token count."
    },
    {
      "id": "configuration-parameters",
      "title": "Configuration parameters",
      "role": "configuration",
      "text": "* **capacity**: Maximum number of tokens in the bucket (controls burst size)\n* **refillRate**: Number of tokens added per refill interval\n* **refillInterval**: Time in seconds between refills\n\nFor example:\n* `capacity=10, refillRate=1, refillInterval=1.0` allows 1 request per second with bursts up to 10\n* `capacity=100, refillRate=10, refillInterval=1.0` allows 10 requests per second with bursts up to 100\n* `capacity=60, refillRate=1, refillInterval=60.0` allows 1 request per minute with bursts up to 60"
    },
    {
      "id": "rate-limit-keys",
      "title": "Rate limit keys",
      "role": "content",
      "text": "The `key` parameter identifies what you're rate limiting. Common patterns:\n\n* **Per user**: `user:{userId}` - Limit each user independently\n* **Per IP address**: `ip:{ipAddress}` - Limit by client IP\n* **Per API endpoint**: `api:{endpoint}:{userId}` - Different limits per endpoint\n* **Global**: `global:api` - Single limit shared across all requests"
    },
    {
      "id": "script-caching-with-evalsha",
      "title": "Script caching with EVALSHA",
      "role": "content",
      "text": "The Java implementation uses [`EVALSHA`](https://redis.io/docs/latest/commands/evalsha) for optimal performance. On first use, the Lua script is loaded into Redis with `SCRIPT LOAD`, and subsequent calls use the cached SHA1 hash. If the script is evicted from the cache, the class automatically falls back to [`EVAL`](https://redis.io/docs/latest/commands/eval) and reloads the script. The script loading uses `volatile` and synchronization to ensure thread safety across multiple threads.\n\n[code example]"
    },
    {
      "id": "thread-safety",
      "title": "Thread safety",
      "role": "content",
      "text": "The `TokenBucket` class is thread-safe. You can share a single instance across your application, including from multiple threads in a servlet container or web framework:\n\n[code example]"
    },
    {
      "id": "running-the-demo",
      "title": "Running the demo",
      "role": "content",
      "text": ""
    },
    {
      "id": "get-the-source-files",
      "title": "Get the source files",
      "role": "content",
      "text": "The demo consists of two Java files. Download them from the [`java-jedis` source folder](https://github.com/redis/docs/tree/main/content/develop/use-cases/rate-limiter/java-jedis) on GitHub, or grab them with `curl`:\n\n[code example]"
    },
    {
      "id": "start-the-demo-server",
      "title": "Start the demo server",
      "role": "content",
      "text": "A demonstration HTTP server is included to show the rate limiter in action\n([source](https://github.com/redis/docs/blob/main/content/develop/use-cases/rate-limiter/java-jedis/DemoServer.java)):\n\n[code example]\n\nThe demo provides an interactive web interface where you can:\n\n* Submit requests and see them allowed or denied in real-time\n* View the current token count\n* Adjust rate limit parameters dynamically\n* Test different rate limiting scenarios\n\nThe demo assumes Redis is running on `localhost:6379` but you can specify a different host and port using the `--redis-host HOST` and `--redis-port PORT` command-line arguments. Visit `http://localhost:8080` in your browser to try it out."
    },
    {
      "id": "response-headers",
      "title": "Response headers",
      "role": "returns",
      "text": "It's common to include rate limit information in HTTP response headers:\n\n[code example]"
    },
    {
      "id": "customization",
      "title": "Customization",
      "role": "content",
      "text": ""
    },
    {
      "id": "using-as-a-servlet-filter",
      "title": "Using as a servlet filter",
      "role": "content",
      "text": "You can wrap the rate limiter as a servlet filter for use with any Java web framework:\n\n[code example]"
    },
    {
      "id": "error-handling",
      "title": "Error handling",
      "role": "errors",
      "text": "The `allow()` method may throw a `JedisException` if the Redis connection is lost. Wrap calls in try/catch blocks for production use:\n\n[code example]"
    },
    {
      "id": "alternative-rate-limiting-algorithms",
      "title": "Alternative rate limiting algorithms",
      "role": "content",
      "text": "The token bucket algorithm above handles most use cases but Redis supports\nother rate limiter patterns that might fit your requirements better. The table\nbelow lists four other algorithms alongside token bucket and summarizes\ntheir features:\n\n| Algorithm | Memory | Accuracy | Burst behavior | Best for |\n|---|---|---|---|---|\n| [Token bucket](#how-it-works) | 1 key (hash) | Exact | Controlled bursts | APIs with bursty traffic |\n| [Fixed window counter](#fixed-window-counter) | 1 key (string) | Approximate | 2x burst at boundaries | Simple API limits |\n| [Sliding window log](#sliding-window-log) | O(n) entries | Exact | No bursts | High-value APIs, audit trails |\n| [Sliding window counter](#sliding-window-counter) | 2 keys (string) | Near-exact | Smoothed boundaries | General-purpose APIs |\n| [Leaky bucket (policing)](#leaky-bucket-policing) | 1 key (hash) | Exact | No bursts | Strict no-burst enforcement |\n\nThe sections below give example implementations of these other algorithms.\nThe three time-based algorithms call `redis.call('TIME')` inside the Lua\nscript to derive the current timestamp from the Redis server clock. This\neliminates clock drift when the limiter runs across multiple application\nservers. The fixed window counter reads no clock: the key's TTL defines\nthe window."
    },
    {
      "id": "fixed-window-counter",
      "title": "Fixed window counter",
      "role": "content",
      "text": "Counts requests within discrete, non-overlapping time intervals.\nSimplest algorithm — one key per window, one `EVAL` round trip.\n\n[code example]\n\n**Trade-off**: A client can make 2x requests by sending `limit` requests\nat the end of one window and `limit` requests at the start of the next."
    },
    {
      "id": "sliding-window-log",
      "title": "Sliding window log",
      "role": "content",
      "text": "Records the exact timestamp of every request in a sorted set.\nProvides a true rolling window with no boundary bursts.\n\n[code example]\n\n**Trade-off**: Memory grows O(n) with request volume. Not ideal for\nhigh-volume, high-cardinality rate limiting."
    },
    {
      "id": "sliding-window-counter",
      "title": "Sliding window counter",
      "role": "content",
      "text": "Blends two fixed-window counters using a weighted average to approximate\na true sliding window. Near-exact accuracy with the same low memory\nfootprint as a fixed window. The two keys use hash tags so they map\nto the same slot in Redis Cluster.\n\n[code example]\n\n**Trade-off**: The weighted estimate may let slightly more or fewer\nrequests through than the exact limit. Negligible for most apps."
    },
    {
      "id": "leaky-bucket-policing",
      "title": "Leaky bucket (policing)",
      "role": "content",
      "text": "A virtual bucket fills with incoming requests and drains at a fixed rate.\nIf the bucket is full, requests are rejected immediately. This is the\npolicing variant — requests are allowed or denied instantly with no delay.\n\n[code example]\n\n**Trade-off**: Overflow traffic is rejected immediately. Clients must\nhandle `429 Too Many Requests` and retry with backoff."
    },
    {
      "id": "learn-more",
      "title": "Learn more",
      "role": "related",
      "text": "* [EVAL command](https://redis.io/docs/latest/commands/eval) - Execute Lua scripts\n* [EVALSHA command](https://redis.io/docs/latest/commands/evalsha) - Execute cached Lua scripts\n* [Lua scripting](https://redis.io/docs/latest/develop/programmability/eval-intro) - Introduction to Redis Lua scripting\n* [HMGET command](https://redis.io/docs/latest/commands/hmget) - Get multiple hash fields\n* [HMSET command](https://redis.io/docs/latest/commands/hmset) - Set multiple hash fields\n* [Jedis client](https://redis.io/docs/latest/develop/clients/jedis) - Redis Java client documentation"
    }
  ],
  "examples": [
    {
      "id": "the-lua-script-ex0",
      "language": "lua",
      "code": "local key = KEYS[1]\nlocal capacity = tonumber(ARGV[1])\nlocal refill_rate = tonumber(ARGV[2])\nlocal refill_interval = tonumber(ARGV[3])\nlocal now = tonumber(ARGV[4])\n\n-- Get current state or initialize\nlocal bucket = redis.call('HMGET', key, 'tokens', 'last_refill')\nlocal tokens = tonumber(bucket[1])\nlocal last_refill = tonumber(bucket[2])\n\n-- Initialize if this is the first request\nif tokens == nil then\n    tokens = capacity\n    last_refill = now\nend\n\n-- Calculate token refill\nlocal time_passed = now - last_refill\nlocal refills = math.floor(time_passed / refill_interval)\n\nif refills > 0 then\n    tokens = math.min(capacity, tokens + (refills * refill_rate))\n    last_refill = last_refill + (refills * refill_interval)\nend\n\n-- Try to consume a token\nlocal allowed = 0\nif tokens >= 1 then\n    tokens = tokens - 1\n    allowed = 1\nend\n\n-- Update state\nredis.call('HMSET', key, 'tokens', tokens, 'last_refill', last_refill)\n\n-- Return result: allowed (1 or 0) and remaining tokens\nreturn {allowed, tokens}",
      "section_id": "the-lua-script"
    },
    {
      "id": "installation-ex0",
      "language": "xml",
      "code": "<dependency>\n      <groupId>redis.clients</groupId>\n      <artifactId>jedis</artifactId>\n      <version>5.2.0</version>\n  </dependency>",
      "section_id": "installation"
    },
    {
      "id": "installation-ex1",
      "language": "groovy",
      "code": "implementation 'redis.clients:jedis:5.2.0'",
      "section_id": "installation"
    },
    {
      "id": "using-the-java-class-ex0",
      "language": "java",
      "code": "import redis.clients.jedis.JedisPool;\n\npublic class Main {\n    public static void main(String[] args) {\n        // Create a Redis connection pool\n        JedisPool jedisPool = new JedisPool(\"localhost\", 6379);\n\n        // Create a rate limiter: 10 requests per second\n        TokenBucket limiter = new TokenBucket(10, 1, 1.0, jedisPool);\n\n        // Check if a request should be allowed\n        TokenBucket.RateLimitResult result = limiter.allow(\"user:123\");\n\n        if (result.allowed()) {\n            System.out.printf(\"Request allowed. %.0f tokens remaining.%n\", result.remaining());\n            // Process the request\n        } else {\n            System.out.println(\"Request denied. Rate limit exceeded.\");\n            // Return 429 Too Many Requests\n        }\n    }\n}",
      "section_id": "using-the-java-class"
    },
    {
      "id": "script-caching-with-evalsha-ex0",
      "language": "java",
      "code": "// The class handles script caching automatically.\n// First call loads the script, subsequent calls use EVALSHA.\nTokenBucket.RateLimitResult result1 = limiter.allow(\"user:123\"); // Uses EVAL + caches\nTokenBucket.RateLimitResult result2 = limiter.allow(\"user:123\"); // Uses EVALSHA (faster)",
      "section_id": "script-caching-with-evalsha"
    },
    {
      "id": "thread-safety-ex0",
      "language": "java",
      "code": "// Create a shared limiter instance\nTokenBucket limiter = new TokenBucket(10, 1, 1.0, jedisPool);\n\n// Safe to call from multiple threads\nExecutorService executor = Executors.newFixedThreadPool(10);\nfor (int i = 0; i < 20; i++) {\n    final int id = i;\n    executor.submit(() -> {\n        TokenBucket.RateLimitResult result = limiter.allow(\"shared:resource\");\n        System.out.printf(\"thread %d: allowed=%b remaining=%.0f%n\",\n                id, result.allowed(), result.remaining());\n    });\n}\nexecutor.shutdown();",
      "section_id": "thread-safety"
    },
    {
      "id": "get-the-source-files-ex0",
      "language": "bash",
      "code": "mkdir rate-limiter-demo && cd rate-limiter-demo\nBASE=https://raw.githubusercontent.com/redis/docs/main/content/develop/use-cases/rate-limiter/java-jedis\ncurl -O $BASE/TokenBucket.java\ncurl -O $BASE/DemoServer.java",
      "section_id": "get-the-source-files"
    },
    {
      "id": "start-the-demo-server-ex0",
      "language": "bash",
      "code": "# Compile\njavac -cp jedis-5.2.0.jar TokenBucket.java DemoServer.java\n\n# Run the demo server\njava -cp .:jedis-5.2.0.jar DemoServer",
      "section_id": "start-the-demo-server"
    },
    {
      "id": "response-headers-ex0",
      "language": "java",
      "code": "int capacity = 10;\ndouble refillInterval = 1.0;\nTokenBucket limiter = new TokenBucket(capacity, 1, refillInterval, jedisPool);\n\nTokenBucket.RateLimitResult result = limiter.allow(\"user:\" + userId);\n\n// Add standard rate limit headers\nresponse.setHeader(\"X-RateLimit-Limit\", String.valueOf(capacity));\nresponse.setHeader(\"X-RateLimit-Remaining\", String.valueOf((int) result.remaining()));\nresponse.setHeader(\"X-RateLimit-Reset\",\n        String.valueOf(System.currentTimeMillis() / 1000 + (long) refillInterval));\n\nif (!result.allowed()) {\n    response.setHeader(\"Retry-After\", String.valueOf((int) refillInterval));\n    response.setStatus(429); // Too Many Requests\n}",
      "section_id": "response-headers"
    },
    {
      "id": "using-as-a-servlet-filter-ex0",
      "language": "java",
      "code": "public class RateLimitFilter implements Filter {\n    private TokenBucket limiter;\n\n    @Override\n    public void init(FilterConfig config) {\n        JedisPool jedisPool = new JedisPool(\"localhost\", 6379);\n        limiter = new TokenBucket(10, 1, 1.0, jedisPool);\n    }\n\n    @Override\n    public void doFilter(ServletRequest req, ServletResponse res, FilterChain chain)\n            throws IOException, ServletException {\n        HttpServletRequest httpReq = (HttpServletRequest) req;\n        HttpServletResponse httpRes = (HttpServletResponse) res;\n\n        String key = \"ip:\" + httpReq.getRemoteAddr();\n        TokenBucket.RateLimitResult result = limiter.allow(key);\n\n        httpRes.setHeader(\"X-RateLimit-Remaining\",\n                String.valueOf((int) result.remaining()));\n\n        if (!result.allowed()) {\n            httpRes.setStatus(429);\n            httpRes.getWriter().write(\"{\\\"error\\\": \\\"Rate limit exceeded\\\"}\");\n            return;\n        }\n        chain.doFilter(req, res);\n    }\n}",
      "section_id": "using-as-a-servlet-filter"
    },
    {
      "id": "error-handling-ex0",
      "language": "java",
      "code": "try {\n    TokenBucket.RateLimitResult result = limiter.allow(\"user:123\");\n    // Handle result\n} catch (JedisException e) {\n    System.err.println(\"Rate limiter error: \" + e.getMessage());\n    // Fail open: allow the request when Redis is unavailable\n    // Or fail closed: deny the request\n}",
      "section_id": "error-handling"
    },
    {
      "id": "fixed-window-counter-ex0",
      "language": "java",
      "code": "import java.util.List;\n\nimport redis.clients.jedis.Jedis;\nimport redis.clients.jedis.JedisPool;\n\npublic class FixedWindowLimiter {\n\n    private static final String SCRIPT = \"\"\"\n            local key    = KEYS[1]\n            local limit  = tonumber(ARGV[1])\n            local window = tonumber(ARGV[2])\n\n            local count = redis.call('INCR', key)\n            if count == 1 then\n                redis.call('EXPIRE', key, window)\n            end\n\n            local ttl = redis.call('PTTL', key)\n\n            if count > limit then\n                return {0, ttl}\n            end\n            return {1, ttl}\n            \"\"\";\n\n    public record Decision(boolean allowed, long retryAfterMs) {}\n\n    @SuppressWarnings(\"unchecked\")\n    public static Decision allow(JedisPool pool, String key, int limit, int windowSeconds) {\n        try (Jedis jedis = pool.getResource()) {\n            List<Long> result = (List<Long>) jedis.eval(\n                    SCRIPT,\n                    List.of(key),\n                    List.of(String.valueOf(limit), String.valueOf(windowSeconds)));\n\n            boolean allowed = result.get(0) == 1L;\n            return new Decision(allowed, allowed ? 0L : result.get(1));\n        }\n    }\n}",
      "section_id": "fixed-window-counter"
    },
    {
      "id": "sliding-window-log-ex0",
      "language": "java",
      "code": "import java.util.List;\nimport java.util.UUID;\n\nimport redis.clients.jedis.Jedis;\nimport redis.clients.jedis.JedisPool;\n\npublic class SlidingWindowLogLimiter {\n\n    private static final String SCRIPT = \"\"\"\n            local key    = KEYS[1]\n            local limit  = tonumber(ARGV[1])\n            local window = tonumber(ARGV[2])\n            local member = ARGV[3]\n\n            local t      = redis.call('TIME')\n            local now    = tonumber(t[1]) + tonumber(t[2]) / 1e6\n            local cutoff = now - window\n\n            redis.call('ZREMRANGEBYSCORE', key, '-inf', cutoff)\n\n            local count = redis.call('ZCARD', key)\n\n            if count < limit then\n                redis.call('ZADD', key, now, member)\n                redis.call('EXPIRE', key, window * 2)\n                return {1, 0}\n            end\n\n            local oldest = redis.call('ZRANGE', key, 0, 0, 'WITHSCORES')\n            local retry_after_ms = 0\n            if oldest[2] then\n                retry_after_ms = math.floor((tonumber(oldest[2]) + window - now) * 1000)\n            end\n\n            return {0, retry_after_ms}\n            \"\"\";\n\n    public record Decision(boolean allowed, long retryAfterMs) {}\n\n    @SuppressWarnings(\"unchecked\")\n    public static Decision allow(JedisPool pool, String key, int limit, int windowSeconds) {\n        try (Jedis jedis = pool.getResource()) {\n            List<Long> result = (List<Long>) jedis.eval(\n                    SCRIPT,\n                    List.of(key),\n                    List.of(String.valueOf(limit),\n                            String.valueOf(windowSeconds),\n                            UUID.randomUUID().toString()));\n\n            return new Decision(result.get(0) == 1L, result.get(1));\n        }\n    }\n}",
      "section_id": "sliding-window-log"
    },
    {
      "id": "sliding-window-counter-ex0",
      "language": "java",
      "code": "import java.util.List;\n\nimport redis.clients.jedis.Jedis;\nimport redis.clients.jedis.JedisPool;\n\npublic class SlidingWindowCounterLimiter {\n\n    private static final String SCRIPT = \"\"\"\n            local base   = KEYS[1]\n            local limit  = tonumber(ARGV[1])\n            local window = tonumber(ARGV[2])\n\n            local t   = redis.call('TIME')\n            local now = tonumber(t[1]) + tonumber(t[2]) / 1e6\n\n            local window_num = math.floor(now / window)\n            local elapsed     = (now % window) / window\n\n            local curr_key = base .. ':' .. window_num\n            local prev_key = base .. ':' .. (window_num - 1)\n\n            local prev = tonumber(redis.call('GET', prev_key) or 0)\n            local curr = tonumber(redis.call('GET', curr_key) or 0)\n\n            local estimate = prev * (1 - elapsed) + curr\n\n            if estimate >= limit then\n                return {0, 0}\n            end\n\n            local new_count = redis.call('INCR', curr_key)\n            if new_count == 1 then\n                redis.call('EXPIRE', curr_key, window * 2)\n            end\n\n            return {1, 0}\n            \"\"\";\n\n    @SuppressWarnings(\"unchecked\")\n    public static boolean allow(JedisPool pool, String key, int limit, int windowSeconds) {\n        try (Jedis jedis = pool.getResource()) {\n            List<Long> result = (List<Long>) jedis.eval(\n                    SCRIPT,\n                    List.of(\"{\" + key + \"}\"),\n                    List.of(String.valueOf(limit), String.valueOf(windowSeconds)));\n\n            return result.get(0) == 1L;\n        }\n    }\n}",
      "section_id": "sliding-window-counter"
    },
    {
      "id": "leaky-bucket-policing-ex0",
      "language": "java",
      "code": "import java.util.List;\n\nimport redis.clients.jedis.Jedis;\nimport redis.clients.jedis.JedisPool;\n\npublic class LeakyBucketLimiter {\n\n    private static final String SCRIPT = \"\"\"\n            local key       = KEYS[1]\n            local capacity  = tonumber(ARGV[1])\n            local leak_rate = tonumber(ARGV[2])\n\n            local t   = redis.call('TIME')\n            local now = tonumber(t[1]) + tonumber(t[2]) / 1e6\n\n            local data      = redis.call('HGETALL', key)\n            local level     = 0\n            local last_leak = now\n\n            if #data > 0 then\n                for i = 1, #data, 2 do\n                    if data[i] == 'level' then\n                        level = tonumber(data[i+1])\n                    elseif data[i] == 'last_leak' then\n                        last_leak = tonumber(data[i+1])\n                    end\n                end\n            end\n\n            local elapsed = now - last_leak\n            level = math.max(0, level - elapsed * leak_rate)\n\n            if level + 1 > capacity then\n                return {0, math.floor((level + 1 - capacity) / leak_rate * 1000)}\n            end\n\n            level = level + 1\n            local ttl = math.ceil(capacity / leak_rate) + 1\n\n            redis.call('HSET', key, 'level', level, 'last_leak', now)\n            redis.call('EXPIRE', key, ttl)\n\n            return {1, 0}\n            \"\"\";\n\n    public record Decision(boolean allowed, long retryAfterMs) {}\n\n    @SuppressWarnings(\"unchecked\")\n    public static Decision allow(JedisPool pool, String key, int capacity, double leakRate) {\n        try (Jedis jedis = pool.getResource()) {\n            List<Long> result = (List<Long>) jedis.eval(\n                    SCRIPT,\n                    List.of(key),\n                    List.of(String.valueOf(capacity), String.valueOf(leakRate)));\n\n            return new Decision(result.get(0) == 1L, result.get(1));\n        }\n    }\n}",
      "section_id": "leaky-bucket-policing"
    }
  ]
}