How to perform attribute based filtering more than 1000 objects in under 50 ms ?
 Performing attribute-based filtering on a large number of objects (e.g., 1000 or more) in a short amount of time (e.g., less than 50 ms) can be challenging, but there are several strategies you can use to optimize the performance of your filtering algorithm:

1. **Pre-processing**: Before filtering, you can pre-process the data to make it more amenable to fast filtering. For example, you can sort the data based on the attribute being filtered, or build an index that allows for fast lookups based on the attribute values.
2. **Parallel processing**: If the filtering criteria can be divided into independent sub-criteria, you can use parallel processing to filter the data in parallel. This can help reduce the overall filtering time by taking advantage of multiple CPU cores or GPUs.
3. **Efficient data structures**: Using efficient data structures such as hash tables, binary search trees, or bitmaps can help speed up the filtering process. For example, if the attribute values are integers, you can use a bitmap to represent the presence or absence of each value, and perform the filtering using bitwise operations.
4. **Filtering heuristics**: You can use heuristics to quickly eliminate objects that are unlikely to pass the filtering criteria. For example, if the filtering criteria involves a range of values, you
The eternal quest for speed!

Attribute-based filtering of a large dataset can be a challenging task, especially when dealing with over 1000 objects. To achieve filtering in under 50 ms, you'll need to employ some clever strategies and data structures. Here are some suggestions to help you accomplish this:

1. **Indexing**: Create an index on the attributes you're filtering on. This can be a simple hash table, a B-Tree, or even a specialized indexing data structure like a Bloom filter. Indexing allows you to quickly look up objects that match the filter criteria.
2. **Caching**: Cache the results of previous filter operations. If the filter criteria are similar or identical, you can reuse the cached results instead of recomputing them. This can be especially useful if the filter criteria are frequently repeated.
3. **Binary Search**: If the objects are sorted by the attribute you're filtering on, you can use binary search to quickly find the objects that match the filter criteria. This reduces the search space to O(log n) instead of O(n).
4. **Bitap Algorithm**: Implement the Bitap algorithm, which is a fast string searching algorithm that can be adapted for attribute-based filtering. It uses bitwise operations to quickly identify matching objects.
5. **SIMD Instructions**: Leverage SIMD (Single Instruction, Multiple Data) instructions, such as those provided by SSE or AVX, to perform filtering operations on multiple objects simultaneously. This can significantly reduce the filtering time