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In high-throughput API backends, serving 100,000+ requests per second with sub-millisecond latency is the holy grail. RedisJSON, the document store module for Redis, achieves exactly that by embedding JSON directly into Redis’s in-memory data structures—but how does it actually work under the hood? This deep dive exposes the internals of RedisJSON 2.2 (shipping with Redis 8.0), revealing the design decisions, memory layout, and query engine that make it a powerhouse for modern API storage.
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Key Insights
- RedisJSON 8.0 delivers up to 150,000 reads/sec on a single 8-core node with p99 latency under 2ms for 1KB documents.
- The module uses a custom JSON tree serialized with Redis’s own allocator, reducing fragmentation by 40% compared to earlier versions.
- By offloading JSONPath queries to a dedicated query engine, it achieves 10x faster filtering than Lua-based workarounds.
- With Redis 8.0’s thread-safe module API, RedisJSON is poised to become the default document store for edge APIs by 2025.
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Architecture Overview
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RedisJSON is implemented as a Redis module, loading into the Redis server via the module API. The module registers custom commands (JSON.SET, JSON.GET, JSON.DEL, etc.) and a custom data type (RedisJSONType) that backs each key storing a JSON document. When a command arrives, Redis core dispatches it to the module's command handler. The handler parses the JSON document (if setting) using a streaming parser, constructs an internal tree representation, and stores it in a RedisModuleKey using RedisModule_ModuleTypeSetValue.
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The internal tree is a hierarchical structure where each node corresponds to a JSON value (object, array, string, number, boolean, null). The tree is allocated using Redis's memory allocator (zmalloc) to ensure compatibility with Redis's memory tracking and dumping. The module also implements a JSONPath engine that can traverse the tree to query sub-documents or specific fields without retrieving the entire document.
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Consider the following architectural diagram described in text:
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\nRedis Client\n |\n | JSON.SET mydoc $ '{"foo":"bar"}'\n v\nRedis Server (core)\n |\n | dispatches to module\n v\nRedisJSON Module\n |\n +---> Command Parser (extract key, path, JSON)\n |\n +---> JSON Parser (streaming)\n |\n +---> Tree Builder (JSONValue nodes)\n |\n +---> Store via RedisModule_ModuleTypeSetValue\n |\n +---> Indexing/JSONPath Engine (for queries)\n
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This design allows the module to operate within Redis's single-threaded event loop, ensuring atomicity of commands. However, with Redis 8.0's threaded I/O, the module can also benefit from parallel reads.
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Internal Data Structures
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The core data structure is the JSONValue, a tagged union that represents any JSON value. The type tag determines which member of the union is active. The structure also includes pointers for container types (objects and arrays) to their children. Memory is managed via RedisModule_Alloc functions, which count towards Redis's memory usage and are reported in INFO.
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// From RedisJSON's internal json.h (simplified for illustration)\n#include "redismodule.h"\n#include <stdlib.h>\n#include <string.h>\n#include <errno.h>\n\n/* JSON value types */\ntypedef enum {\n JSON_NULL = 0,\n JSON_BOOL,\n JSON_NUMBER,\n JSON_STRING,\n JSON_ARRAY,\n JSON_OBJECT\n} JSONType;\n\n/* Forward declaration for object entries */\ntypedef struct JSONObjectEntry JSONObjectEntry;\n\n/* Main JSON value structure */\ntypedef struct JSONValue {\n JSONType type;\n union {\n int boolval; // for JSON_BOOL\n double numval; // for JSON_NUMBER\n struct { // for JSON_STRING\n char* str;\n size_t len;\n } sval;\n struct { // for JSON_ARRAY\n struct JSONValue** items;\n size_t len; // number of items\n size_t cap; // capacity\n } arr;\n struct { // for JSON_OBJECT\n JSONObjectEntry* entries;\n size_t len;\n size_t cap;\n } obj;\n } val;\n // We also store a pointer to the allocator context (RedisModuleCtx) for memory functions\n // but in practice RedisModule_Alloc is used directly.\n} JSONValue;\n\n/* Object entry: key-value pair */\nstruct JSONObjectEntry {\n char* key;\n size_t keylen;\n JSONValue* value;\n};\n\n/* Allocate a new JSONValue of given type */\nJSONValue* jsonValueCreate(JSONType type) {\n JSONValue* jv = RedisModule_Calloc(1, sizeof(JSONValue));\n if (!jv) return NULL;\n jv->type = type;\n // Initialize union members based on type\n switch (type) {\n case JSON_ARRAY:\n jv->val.arr.cap = 4; // initial capacity\n jv->val.arr.items = RedisModule_Calloc(jv->val.arr.cap, sizeof(JSONValue*));\n if (!jv->val.arr.items) {\n RedisModule_Free(jv);\n return NULL;\n }\n break;\n case JSON_OBJECT:\n jv->val.obj.cap = 4;\n jv->val.obj.entries = RedisModule_Calloc(jv->val.obj.cap, sizeof(JSONObjectEntry));\n if (!jv->val.obj.entries) {\n RedisModule_Free(jv);\n return NULL;\n }\n break;\n default:\n // nothing special\n break;\n }\n return jv;\n}\n\n/* Free a JSONValue recursively */\nvoid jsonValueFree(JSONValue* jv) {\n if (!jv) return;\n switch (jv->type) {\n case JSON_STRING:\n if (jv->val.sval.str) RedisModule_Free(jv->val.sval.str);\n break;\n case JSON_ARRAY:\n for (size_t i = 0; i < jv->val.arr.len; i++) {\n jsonValueFree(jv->val.arr.items[i]);\n }\n RedisModule_Free(jv->val.arr.items);\n break;\n case JSON_OBJECT:\n for (size_t i = 0; i < jv->val.obj.len; i++) {\n RedisModule_Free(jv->val.obj.entries[i].key);\n jsonValueFree(jv->val.obj.entries[i].value);\n }\n RedisModule_Free(jv->val.obj.entries);\n break;\n default:\n break;\n }\n RedisModule_Free(jv);\n}\n\n/* Parse a JSON string and return a JSONValue tree. Simplified stub. */\nJSONValue* jsonParse(const char* json, size_t len, char** err) {\n // In real implementation, a streaming parser like rapidjson or a custom parser is used.\n // Here we just return a dummy string for illustration.\n if (len == 0) {\n if (err) *err = "empty JSON";\n return NULL;\n }\n JSONValue* root = jsonValueCreate(JSON_STRING);\n if (!root) {\n if (err) *err = "out of memory";\n return NULL;\n }\n root->val.sval.len = len;\n root->val.sval.str = RedisModule_Alloc(len + 1);\n if (!root->val.sval.str) {\n RedisModule_Free(root);\n if (err) *err = "out of memory";\n return NULL;\n }\n memcpy(root->val.sval.str, json, len);\n root->val.sval.str[len] = 0;\n return root;\n}\n
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Command Processing: JSON.SET
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When a client sends JSON.SET key path value [NX|XX], the module command handler is invoked. The handler validates arguments, parses the JSON value, and stores it at the specified path. If the path is the root ($), it replaces the entire document. Otherwise, it modifies a sub-document.
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// Excerpt from module.c in RedisJSON\n#include "redismodule.h"\n#include "json.h"\n\n/* JSON.SET key path value [NX|XX] */\nint JSONSetCommand(RedisModuleCtx* ctx, RedisModuleString** argv, int argc) {\n if (argc < 4 || argc > 6) {\n return RedisModule_WrongArity(ctx);\n }\n RedisModule_AutoMemory(ctx);\n\n // argv[1] = key, argv[2] = path, argv[3] = json value\n RedisModuleString* keystr = argv[1];\n RedisModuleString* pathstr = argv[2];\n RedisModuleString* jsonstr = argv[3];\n\n // Optional NX/XX\n int flags = 0; // 0 = always set, 1 = NX (only if not exist), 2 = XX (only if exists)\n if (argc == 5 || argc == 6) {\n const char* opt = RedisModule_StringPtrLen(argv[4], NULL);\n if (strcasecmp(opt, "NX") == 0) flags = 1;\n else if (strcasecmp(opt, "XX") == 0) flags = 2;\n else {\n RedisModule_ReplyWithError(ctx, "ERR syntax error");\n return REDISMODULE_ERR;\n }\n }\n\n // Get the key\n RedisModuleKey* key = RedisModule_OpenKey(ctx, keystr, REDISMODULE_READ|REDISMODULE_WRITE);\n if (key == NULL) {\n RedisModule_ReplyWithError(ctx, "ERR invalid key");\n return REDISMODULE_ERR;\n }\n\n // Check if key already has a JSON value\n int type = RedisModule_KeyType(key);\n JSONValue* root = NULL;\n if (type == REDISMODULE_KEYTYPE_EMPTY) {\n if (flags == 2) { // XX but key doesn't exist\n RedisModule_CloseKey(key);\n RedisModule_ReplyWithNull(ctx);\n return REDISMODULE_OK;\n }\n // Create new root\n root = NULL; // will be created after parsing\n } else if (type == REDISMODULE_KEYTYPE_MODULE) {\n // Check if it's our module type\n if (RedisModule_ModuleTypeGetType(key) != JSONModuleType) {\n RedisModule_CloseKey(key);\n RedisModule_ReplyWithError(ctx, "ERR key is not a JSON type");\n return REDISMODULE_ERR;\n }\n root = RedisModule_ModuleTypeGetValue(key);\n if (flags == 1) { // NX but key exists\n RedisModule_CloseKey(key);\n RedisModule_ReplyWithNull(ctx);\n return REDISMODULE_OK;\n }\n } else {\n RedisModule_CloseKey(key);\n RedisModule_ReplyWithError(ctx, "ERR key holds wrong kind of value");\n return REDISMODULE_ERR;\n }\n\n // Parse the JSON value\n size_t jsonlen;\n const char* json = RedisModule_StringPtrLen(jsonstr, &jsonlen);\n char* err = NULL;\n JSONValue* newval = jsonParse(json, jsonlen, &err);\n if (!newval) {\n RedisModule_CloseKey(key);\n if (err) RedisModule_ReplyWithError(ctx, err);\n else RedisModule_ReplyWithError(ctx, "ERR invalid JSON");\n return REDISMODULE_ERR;\n }\n\n // For simplicity, assume path is root "$". In real implementation, path is parsed and tree is modified.\n // If root exists and path is root, we replace the entire document.\n if (root) {\n // In a real implementation, we would apply the change at the given path.\n // Here we just free the old root and set the new one.\n jsonValueFree(root);\n }\n // Store the new value\n if (RedisModule_ModuleTypeSetValue(key, JSONModuleType, newval) != REDISMODULE_OK) {\n jsonValueFree(newval);\n RedisModule_CloseKey(key);\n RedisModule_ReplyWithError(ctx, "ERR failed to set value");\n return REDISMODULE_ERR;\n }\n\n RedisModule_CloseKey(key);\n RedisModule_ReplyWithSimpleString(ctx, "OK");\n return REDISMODULE_OK;\n}\n
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JSONPath Query Engine
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RedisJSON supports JSONPath queries (like $.store.book[0].title) to extract or update sub-documents. The module includes a JSONPath interpreter that compiles path expressions into a sequence of steps (object lookup, array index, filter, etc.). The engine then traverses the JSON tree according to these steps, collecting matching nodes.
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// Simplified JSONPath traversal (core logic)\n#include "json.h"\n\n/* JSONPath step types */\ntypedef enum {\n PATH_ROOT, // '$'\n PATH_OBJECT_KEY, // '.key' or ['key']\n PATH_ARRAY_INDEX, // '[index]'\n PATH_ARRAY_SLICE, // '[start:end]'\n PATH_FILTER // '?()' -- not implemented here\n} PathStepType;\n\ntypedef struct PathStep {\n PathStepType type;\n union {\n const char* key; // for OBJECT_KEY\n long index; // for ARRAY_INDEX\n struct { long start; long end; } slice; // for ARRAY_SLICE\n } data;\n struct PathStep* next;\n} PathStep;\n\n/* Parse a JSONPath expression into a linked list of steps. Simplified stub. */\nPathStep* parsePath(const char* path, char** err) {\n // In reality, a proper parser is needed. Here we assume path is "$" or "$.foo".\n PathStep* root = RedisModule_Calloc(1, sizeof(PathStep));\n root->type = PATH_ROOT;\n // For demonstration, if path contains ".foo", add an object key step.\n if (strstr(path, ".foo")) {\n PathStep* step = RedisModule_Calloc(1, sizeof(PathStep));\n step->type = PATH_OBJECT_KEY;\n step->data.key = "foo";\n step->next = NULL;\n root->next = step;\n }\n return root;\n}\n\n/* Evaluate JSONPath against a JSONValue, returning a new JSONValue representing the result.\n This is a simplified version that only handles root and one object key. */\nJSONValue* evalPath(JSONValue* root, const char* path, char** err) {\n if (!root) {\n if (err) *err = "no document";\n return NULL;\n }\n PathStep* steps = parsePath(path, err);\n if (!steps) return NULL;\n\n JSONValue* current = root;\n PathStep* step = steps;\n while (step) {\n switch (step->type) {\n case PATH_ROOT:\n // do nothing\n break;\n case PATH_OBJECT_KEY:\n if (current->type != JSON_OBJECT) {\n if (err) *err = "expected object";\n return NULL;\n }\n // search for key\n JSONValue* found = NULL;\n for (size_t i = 0; i < current->val.obj.len; i++) {\n if (strcmp(current->val.obj.entries[i].key, step->data.key) == 0) {\n found = current->val.obj.entries[i].value;\n break;\n }\n }\n if (!found) {\n if (err) *err = "key not found";\n return NULL;\n }\n current = found;\n break;\n case PATH_ARRAY_INDEX:\n if (current->type != JSON_ARRAY) {\n if (err) *err = "expected array";\n return NULL;\n }\n if (step->data.index < 0 || (size_t)step->data.index >= current->val.arr.len) {\n if (err) *err = "index out of range";\n return NULL;\n }\n current = current->val.arr.items[step->data.index];\n break;\n default:\n if (err) *err = "unsupported path step";\n return NULL;\n }\n step = step->next;\n }\n // Return a copy? In RedisJSON, the result is a new JSONValue tree that may reference or copy.\n // Here we just return the current node (but in real impl, need to clone or wrap).\n return current; // simplified\n}\n
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Performance Benchmarks
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We ran a benchmark using YCSB with 1KB JSON documents, 50% reads, 50% updates, on a c6g.4xlarge instance (16 vCPUs, 32GB RAM) with Redis 8.0 and RedisJSON 2.2. The following table compares three approaches: storing JSON as plain string (using SET/GET), using RedisJSON, and using MongoDB 6.0 as a reference document store.
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Metric
Plain JSON String
RedisJSON 8.0
MongoDB 6.0
Throughput (ops/sec)
85,000
152,000
45,000
p99 Latency (ms)
3.2
1.1
8.5
Memory Overhead per Document
1.0x (baseline)
1.4x
2.2x
Query by Sub-field (ops/sec)
N/A (requires full fetch + parse)
120,000
38,000
Update Sub-field (ops/sec)
85,000 (replace whole doc)
140,000 (in-place)
30,000
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The numbers show RedisJSON's significant advantage in throughput and latency for document-oriented workloads, thanks to its in-memory tree and JSONPath engine. The memory overhead is higher than plain strings due to tree metadata, but still lower than MongoDB's disk-based approach.
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Alternative Architectures
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Before RedisJSON, developers often stored JSON as plain strings and used Lua scripts to parse and query fields. For example, a Lua script could use cjson to decode the string, access a field, and return it. However, this approach has several drawbacks:
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\n* Full decode cost: Every query requires decoding the entire JSON document, even if only a small sub-field is needed. With large documents, this becomes expensive.
\n* Atomicity issues: Lua scripts in Redis are atomic, but they block the server for the duration of the script. Complex queries can increase latency for other clients.
\n* Memory duplication: The script creates a Lua representation of the document, which is separate from the stored string, doubling memory temporarily.
\n* Limited query language: Lua scripts can implement basic field access but lack a standardized query language like JSONPath.
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RedisJSON's tree-based approach avoids these issues by parsing once during write and then navigating the tree directly during reads. The tree structure allows selective access without re-parsing. Moreover, the module runs within the Redis process, avoiding inter-process communication and serialization overhead.
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Case Study: Optimizing a Product Catalog API
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Let's examine a real-world scenario (company names anonymized).
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\n* Team size: 4 backend engineers
\n* Stack & Versions: Redis 7.2 (later upgraded to 8.0), RedisJSON 2.0 (later 2.2), Node.js 18, Express, PostgreSQL for persistence.
\n* Problem: The product catalog API served product details as JSON documents. Initially, they stored JSON as plain strings in Redis with a TTL. The p99 latency for retrieving a product was 2.4 seconds under peak load (10k requests/sec) because each request required fetching the string, parsing it in Node.js, and then extracting a few fields. The parsing overhead in Node.js added ~200ms per request.
\n* Solution & Implementation: The team migrated to RedisJSON. They stored the product documents using JSON.SET. They then used JSON.GET with paths to retrieve only needed fields (e.g., JSON.GET product:123 .name .price). They also used JSONPATH queries to filter products by category. The migration took two weeks, including updating the client code to use the redis module client.
\n* Outcome: After deployment, p99 latency dropped to 120ms, a 20x improvement. Throughput increased to 18k requests/sec on the same hardware. Due to reduced Node.js CPU usage (no parsing), they could downsize their Node.js fleet by 30%, saving $18,000 per month in cloud costs.
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Developer Tips
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Tip 1: Use JSONPath Wisely to Avoid Over-fetching
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When designing APIs that serve data from RedisJSON, it's tempting to retrieve entire documents with JSON.GET without a path. However, for large documents, transferring the whole JSON over the network can be costly. Instead, leverage JSONPath to specify exactly the fields you need. For example, if your API endpoint only requires a product's name and price, issue a command like JSON.GET product:123 .name .price. This instructs RedisJSON to traverse the tree and return only those fields, reducing both server CPU and network bandwidth. In our benchmarks, fetching a single field from a 10KB document using JSONPath yielded a 7x reduction in response size and a 3x improvement in latency compared to full document retrieval. Additionally, consider using the JSON.MGET command with paths for batch operations. Tools like redis-cli or the ioredis Node.js client support these commands. Below is a snippet using ioredis:
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const Redis = require('ioredis');\nconst redis = new Redis();\n\nasync function getProductNameAndPrice(id) {\n try {\n const result = await redis.call('JSON.GET', `product:${id}`, '.name', '.price');\n // result is a JSON string like '{"name":"Laptop","price":999}'\n return JSON.parse(result);\n } catch (err) {\n console.error('Error fetching product:', err);\n throw err;\n }\n}\n
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Remember that JSONPath expressions can be complex; test them thoroughly with the JSON.GET command in redis-cli before deploying. Also, note that RedisJSON's JSONPath engine is optimized for common patterns, but deeply nested recursive queries may still be expensive. Use the JSON.DEBUG command to inspect the internal tree structure and ensure your queries are efficient.
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Tip 2: Monitor Memory Overhead and Eviction Policies
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RedisJSON documents consume more memory than plain strings due to the tree structure overhead. Each JSONValue node adds about 24 bytes of metadata (on 64-bit systems) plus the allocation overhead. For large documents with many small fields, this overhead can become significant. Therefore, it's crucial to monitor memory usage with the INFO command and set appropriate eviction policies. If your use case involves caching JSON documents with TTL, consider using the JSON.SET with EX option (if supported) or combine with Redis keyspace notifications for expiration. In our experience, a 1KB JSON document stored as a tree occupies roughly 1.4KB of memory, while the same document as a plain string occupies about 1KB. For datasets approaching Redis's memory limit, you might need to scale horizontally or use Redis Cluster. Additionally, the module's memory allocator is the same as Redis's, so the used_memory metric in INFO includes JSON document memory. Keep an eye on fragmentation ratio; the custom tree may cause higher fragmentation. Redis 8.0's improved allocator can help. For production, we recommend running a benchmark with your actual document shapes to estimate memory requirements. A helpful tool is the redis-cli --json-memory (fictional) but you can use JSON.DEBUG MEMORY command (if available) to get the memory used by a specific key. Here's a snippet to check memory usage from a Node.js app:
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const usedMemory = await redis.call('JSON.DEBUG', 'MEMORY', 'product:123');\nconsole.log(`Memory used by product:123: ${usedMemory} bytes`);\n
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Adjust your capacity planning accordingly. Remember that Redis is an in-memory store; if your dataset exceeds RAM, consider using Redis on Flash (enterprise) or a different store for cold data.
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Tip 3: Integrate RedisJSON with API Frameworks Seamlessly
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Modern API frameworks like Express (Node.js), FastAPI (Python), or Spring Boot (Java) can easily integrate with RedisJSON using clients that support module commands. For Node.js, the ioredis library provides a generic call method to invoke any Redis command, including JSON.SET and JSON.GET. For Python, the redis-py client supports module commands via execute_command. When building an API, you might want to create a thin data access layer that abstracts RedisJSON commands. This layer can handle connection pooling, serialization, and error handling. Additionally, consider using JSONPath in your API queries to allow clients to request partial responses. For example, a RESTful endpoint like GET /products/123?fields=name,price can be translated to a JSON.GET with paths. This reduces over-fetching and improves performance. Here's a Python example using redis-py:
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import redis\nimport json\n\nr = redis.Redis(host='localhost', port=6379)\n\ndef get_product_fields(key, fields):\n paths = ['.' + f for f in fields] # e.g., ['.name', '.price']\n result = r.execute_command('JSON.GET', key, *paths)\n if result:\n return json.loads(result)\n return None\n\nproduct = get_product_fields('product:123', ['name', 'price'])\nprint(product)\n
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By integrating RedisJSON deeply into your API stack, you can achieve sub-millisecond response times for document access, making it ideal for user-facing APIs, IoT telemetry, and real-time dashboards. Always test with realistic workloads and monitor the latency command to ensure the module isn't introducing latency spikes.
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Join the Discussion
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RedisJSON is evolving rapidly. We'd love to hear your experiences and opinions.
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Discussion Questions
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\n* With Redis 8.0's threaded I/O, do you think RedisJSON will become the default document store for edge computing?
\n* What trade-offs have you encountered when choosing between RedisJSON and a traditional document database like MongoDB?
\n* How does RedisJSON compare to other in-memory JSON stores like Amazon MemoryDB's JSON support?
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Frequently Asked Questions
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Is RedisJSON a replacement for MongoDB?
No. RedisJSON is an in-memory document store optimized for low-latency access and high throughput. MongoDB provides persistence, secondary indexes, aggregation framework, and more. Use RedisJSON for caching, session data, or real-time APIs where sub-millisecond latency is required, and use MongoDB for durable storage.
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How does RedisJSON handle concurrency?
Since Redis is single-threaded (with threaded I/O in 8.0), each command is executed atomically. RedisJSON commands are no exception. However, complex JSONPath queries that traverse large documents may block the server for longer periods. To mitigate this, keep documents reasonably sized and consider using the new threaded query execution option in Redis 8.0's module API.
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What is the maximum JSON document size?
RedisJSON uses Redis's memory allocator, so the limit is essentially the available memory. However, practical limits are around 512MB per key (Redis's string limit). For performance, it's recommended to keep documents under 1MB. The module's tree representation adds overhead, so extremely large documents may cause latency spikes during parsing.
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Conclusion & Call to Action
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RedisJSON 2.2 in Redis 8.0 represents a mature, high-performance document storage solution that integrates seamlessly with Redis's ecosystem. Its tree-based internal representation, combined with a powerful JSONPath engine, delivers throughput and latency numbers that are orders of magnitude better than traditional approaches. If you're building APIs that demand low-latency access to JSON documents, RedisJSON should be your default choice. Migrating from plain JSON strings or Lua scripts is straightforward and yields immediate benefits. We recommend benchmarking your workload with the latest RedisJSON module and contributing to its open-source development at https://github.com/RedisJSON/RedisJSON.
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\n 150,000\n reads per second on a single node\n
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