Comparison of distributed self-increasing ID solutions implemented by Redis

In distributed application development, the generation of self-increasing ID is a common requirement. In a stand-alone environment, you can use the database's auto-increment primary key to implement auto-increment ID. However, in a distributed environment, using auto-increment primary keys will cause duplication. Therefore, other solutions need to be used to ensure the uniqueness of auto-increment IDs.

Redis is a high-performance in-memory database that can implement distributed self-increasing ID solutions. In this article, we will introduce three common Redis distributed self-increasing ID solutions and compare them to help developers choose the solution that suits their projects.

Based on the redis incr command

Redis provides an incr command that can auto-increment the specified key and return the auto-incremented value. When using the incr command to generate an auto-increment ID, you can set the key to a fixed string and increment the string with each operation.

The main code for using Redis to generate a distributed auto-increment ID scheme is as follows:

from redis import StrictRedis

redis = StrictRedis(host='localhost', port=6379, db=0)

def get_next_id():
    return redis.incr('id_generator')

Since the incr command of Redis is an atomic operation, it can ensure that the generated ID will be generated when multiple clients access it at the same time. only one.

The solution based on the redis incr command is very simple, but it has a fatal flaw: the ID will continue to increase by itself. When the maximum available value of Redis is reached (the default is 2^31-1), an error will be returned. . This means that if the service is not restarted for a long time after the system comes online, the ID will be unavailable, which may result in data loss or data discontinuity.

Based on redis script Lua script

In order to avoid the problem of Redis self-increasing ID being unavailable for a long time, we can use Lua script to control the range of self-increasing ID. Lua scripts can complete multiple operations in one atomic operation, which makes it possible to specify the range in which the auto-increment ID should be generated based on business requirements when generating an auto-increment ID, instead of continuing to increment it all the time.

The following is the code for the Redis distributed auto-increment ID scheme implemented based on Lua script:

from redis import StrictRedis

redis = StrictRedis(host='localhost', port=6379, db=0)

SCRIPT = """
local name = KEYS[1]
local start = tonumber(ARGV[1])
local stop = tonumber(ARGV[2])
if redis.call('exists', name) == 0 then
    redis.call('set', name, start)
    return tonumber(start)
end
local id = redis.call('incr', name)
if id < stop then
    return tonumber(id)
else
    redis.call('set', name, start)
    return tonumber(start)
end
"""

def get_next_id(start, stop):
    result = redis.eval(script=SCRIPT, keys=['id_generator'], args=[start, stop])
    return result

In this Lua script, we define two parameters start and stop, which are used to control the automatic Increase the ID generation range. If the key id_generator does not exist in Redis, initialize it to start and return to start; otherwise, use the incr command of Redis to increment the id_generator and determine whether the incremented value exceeds the stop value. If it exceeds, reset the value of id_generator to start and return to start; otherwise, return the new generated ID.

This implementation based on Lua script can flexibly control the range of auto-incremented ID generation, but it is more complicated to implement. You need to use the Redis eval command to execute the Lua script and pass parameters.

Based on redis Redlock

Redlock is a distributed lock solution provided by Redis, which can ensure that the same resource can only be accessed by one client at the same time in a distributed environment. We can use Redlock to implement a distributed auto-increment ID scheme to ensure that the generated auto-increment ID is unique.

The main code for using Redlock to implement the distributed self-increasing ID scheme is as follows:

from redis import StrictRedis
from redlock import Redlock

redis = StrictRedis(host='localhost', port=6379, db=0)
redlock = Redlock([{"host": "localhost", "port": 6379, "db": 0}], retry_times=3)

def get_next_id():
    with redlock.lock('id_lock', 1000):
        return redis.incr('id_generator')

By using Redlock to implement the distributed self-increasing ID scheme, we can avoid the need for multiple clients to access the ID at the same time. Generate duplicate problems, and can lock when generating auto-increment IDs to prevent thread safety issues.

However, since locking operations consume a lot of time and resources, the performance of Redlock may decrease in high concurrency scenarios.

Comparative analysis

Three Redis implementations of distributed self-increasing ID solutions have their own advantages and disadvantages. Let’s analyze their comparison:

1. Based on the redis incr command

Advantages: Simple to implement, convenient and fast.

Disadvantages: The ID will continue to increase by itself, and problems such as ID unavailability, data loss or data discontinuity may occur.

Applicable scenarios: simple business scenarios, which do not require high continuity of data ID.

2. Based on redis script Lua script

Advantages: The generation range of auto-incremented IDs can be flexibly controlled to ensure data continuity.

Disadvantages: The implementation is complicated and you need to use the Redis eval command to execute the Lua script and pass parameters.

Applicable scenarios: Scenarios with strict requirements on data ID continuity and business logic, such as e-commerce, finance, etc.

3. Based on redis Redlock

Advantages: The locking operation ensures thread safety and avoids the problem of repeated data generation.

Disadvantages: Since locking operations consume a lot of time and resources, performance may decrease in high concurrency scenarios.

Applicable scenarios: scenarios with high concurrency, distribution, and high requirements for the continuity of data IDs.

Conclusion

Based on the above comparative analysis, we can draw the following conclusions:

1. The two solutions based on redis incr command and based on redis Redlock have relatively narrow scope of application. Not applicable to all scenarios.
2. The Lua script solution based on redis script can flexibly control the range of auto-incremented ID generation, and is suitable for scenarios that require data ID continuity and high business logic requirements.

Therefore, when choosing Redis to implement a distributed self-increasing ID solution, you need to consider the specific needs of the business scenario and choose an appropriate solution.

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