Redis with Java (Jedis, Lettuce) - Time & Space Complexity
When using Redis with Java clients like Jedis or Lettuce, it is important to understand how the time cost of commands grows as data size increases.
We want to know how the number of operations changes when we run commands on Redis through Java.
Analyze the time complexity of the following Redis commands executed via Java.
// Using Jedis client in Java
Jedis jedis = new Jedis("localhost");
// Add 1000 items to a Redis list
for (int i = 0; i < 1000; i++) {
jedis.lpush("mylist", "value" + i);
}
// Retrieve all items
List<String> items = jedis.lrange("mylist", 0, -1);
This code pushes 1000 values into a Redis list and then fetches all items from it.
Look at what repeats in this code.
- Primary operation: The loop calls
lpush1000 times to add items. - How many times: Exactly 1000 times for pushing, then one
lrangeto get all items.
As the number of items to add grows, the number of lpush calls grows the same way.
| Input Size (n) | Approx. Operations |
|---|---|
| 10 | 10 lpush calls + 1 lrange |
| 100 | 100 lpush calls + 1 lrange |
| 1000 | 1000 lpush calls + 1 lrange |
Pattern observation: The number of lpush calls grows directly with input size, while lrange is a single call but returns all data.
Time Complexity: O(n)
This means the total time grows linearly with the number of items you add or retrieve.
[X] Wrong: "Calling lpush multiple times is just one operation because it's the same command repeated."
[OK] Correct: Each lpush call is a separate network request and Redis operation, so the total time adds up with each call.
Understanding how Redis commands scale when used from Java clients helps you explain performance in real projects and shows you can reason about backend efficiency.
What if we used a Redis pipeline to send all lpush commands at once? How would the time complexity change?