i am building a network intrusion detection system using visual c++ and one of its component is a tcp session manager for the whole network. The tcp session data is stored in a concurrent vector so that it can be accessed from other threads. I found out there is no easy way to remove a session from the concurrent vector once the session is closed. So my QUESTION is what is the smartest method you know of for removing items from concurrent vectors.
Smart == fairly easy to program without too much performance hit
Thankyou
I'd consider using a std::set instead of a vector here - especially if the number of items stored is large. I imagine you'll also want to perform lookups frequently.
Search and removal from the set is O(log(n)) complexity rather than O(n) for the std::vector - although the trivial insertion case in the is O(1) rather than O(log(n)) with the set.
You will also need mutex to protect all of these operations.
Related
I'm currently playing around with RocksDB (C++) and was curious about some performance metrics I've experienced.
For testing purposes, my database keys are file paths and the values are filenames. My database has around 2M entries in it. I'm running RocksDB locally on a MacBook Pro 2016 (SSD).
My use case is dominated by reads. Full key scans are quite common as are key scans that include a "significant" number of keys. (50%+)
I'm curious about the following observations:
1. An Iterator is dramatically faster than calling Get when performing full key scans.
When I want to look at all of the keys in the database, I'm seeing a 4-8x performance improvement when using an Iterator instead of calling Get for each key. The use of MultiGet makes no difference.
In the case of calling Get roughly 2M times, the keys have been previously fetched into a vector and sorted lexicographically. Why is calling Get repeatedly so much slower than using an Iterator? Is there a way to narrow the performance gap between the two APIs?
2. When fetching around half the keys, the performance between using an Iterator and Get starts to become negligible.
As the number of keys to fetch is reduced, then making multiple calls to Get starts to take about as long as using an Iterator as the iterator is paying the price of scanning over keys that aren't in the desired keyset.
Is there some "magic" ratio where this becomes true for most databases? For example, if I need to scan over 25% of the keys, then calling Get is faster, but if it's 75% of the keys, then an Iterator is faster. But those numbers are just "made up" by rough testing.
3. Fetching keys in sorted order does not appear to improve performance.
If I pre-sort the keys I want to fetch into the same order that an Iterator would return them in, that does not appear to make calling Get multiple times any faster. Why is that? It's mentioned in the documentation that it's recommended to sort keys before doing a batch insert. Does Get not benefit from the same look-ahead caching that an Iterator benefits from?
4. What settings are recommended for a read-heavy use case?
Finally, are there any specific settings recommended for a read-heavy use case that might involve scanning a significant number of keys at once?
macOS 10.14.3, MacBook Pro 2016 SSD, RocksDB 5.18.3, Xcode 10.1
RocksDB internally represents its data as a log-structured merge tree which has several sorted layers by default (this can be changed with plugins/config). The intuition from Paul's first answer holds, except there is no classical index; the data is actually sorted on disk with pointers to the next files. The lookup operation has on average logarithmic complexity, but advancing an iterator in a sorted range is constant time. So for dense sequential reads, iterating is much faster.
The point where the costs balance out is determined not only by the number of keys you read, but also by the size of the database. As the database grows, the lookup becomes slower, while Next() remains constant. Very recent inserts are likely to be read very fast, since they may still be in memory (memtables).
Sorting the keys actually just improves your cache hit-rate. Depending on your disk, the difference may be very small, e.g., if you have an NVMe SSD, the difference in access time is just not as drastic anymore as it was when it was RAM vs. HDD. If you have to do several operations over the same or even different key-sets doing them by key-order (f(a-c) g(a-c) f(d-g)...) instead of sequentially should improve your performance, since you will have more cache-hits and also benefit from the RocksDB block cache.
The tuning guide is a good starting point, especially the video on database solutions, but if RocksDB is too slow for you also consider using a DB based on a different storage algorithm. LSM is typically better for write-heady workloads, and while RocksDB lets you control read vs. write vs. space amplification very well, a b-tree or ISAM based solution may just be much faster for range-reads/repeated reads.
I don't know anything about RocksDB per-se, but I can answer a lot of this from first principles.
An Iterator is dramatically faster than calling Get when performing full key scans.
This is likely to be because Get has to do a full lookup in the underlying index (starting from the top) whereas advancing an iterator can be achieved by just moving from the current node to the next. Assuming the index is implemented as a red-black tree or similar, there's a lot less work in the second method than the first.
When fetching around half the keys, the performance between using an Iterator and Get starts to become negligible.
So you are skipping entries by calling iterator->Next () multiple times? If so, then there will come a point where it's cheaper to call Get for each key instead, yes. Exactly when that happens will depend on the number of entries in the index (since that determines the number of levels in the tree).
Fetching keys in sorted order does not appear to improve performance.
No, I would not expect it to. Get is (presumably) stateless.
What settings are recommended for a read-heavy use case?
That I don't know, sorry, but you might read:
https://github.com/facebook/rocksdb/wiki/RocksDB-Tuning-Guide
I was soving a competitive programming problem with the following requirements:
I had to maintain a list of unqiue 2d points (x,y), the number of unique points would be less than 500.
My idea was to store them in a hash table (C++ unordered set to be specific) and each time a node turned up i would lookup the table and if the node is not already there i would insert it.
I also know for a fact that i wouldn't be doing more than 500 lookups.
So i saw some solutions simply searching through an array (unsorted) and checking if the node was already there before inserting.
My question is is there any reasonable way to guess when should i use a hash table over a manual search over keys without having to benchmark them?
My question is is there any reasonable way to guess when should i use a hash table over a manual search over keys without having to benchmark them?
I am guessing you are familiar with basic algorithmics & time complexity and C++ standard containers and know that with luck hash table access is O(1)
If the hash table code (or some balanced tree code, e.g. using std::map - assuming there is an easy order on keys) is more readable, I would prefer it for that readability reason alone.
Otherwise, you might make some guess taking into account the approximate timing for various operations on a PC. BTW, the entire http:///norvig.com/21-days.html page is worth reading.
Basically, memory accesses are much more slow than everything else in the CPU. The CPU cache is extremely important. A typical memory access with cache fault requiring fetching data from DRAM modules is several hundreds times slower than some elementary arithmetic operation or machine instruction (e.g. adding two integers in registers).
In practice, it does not matter that much, as long as your data is tiny (e.g. less than a thousand elements), since in that case it is likely to sit in L2 cache.
Searching (linearly) in an array is really fast (since very cache friendly), up to several thousand of (small) elements.
IIRC, Herb Sutter mentions in some video that even inserting an element inside a vector is practically -but unintuitively- faster (taking into account the time needed to move slices) than inserting it into some balanced tree (or perhaps some other container, e.g. an hash table), up to a container size of several thousand small elements. This is on typical tablet, desktop or server microprocessor with a multimegabyte cache. YMMV.
If you really care that much, you cannot avoid benchmarking.
Notice that 500 pairs of integers is probably fitting into the L1 cache!
My rule of thumb is to assume the processor can deal with 10^9 operations per second.
In your case there are only 500 entries. An algorithm up to O(N^2) could be safe. By using contiguous data structure like vector you can leverage the fast cache hit. Also hash function sometimes can be costly in terms of constant. However if you have a data size of 10^6, the safe complexity might be only O(N) in total. In this case you might need to consider O(1) hashmap for a single lookup.
You can use Big O Complexity to roughly estimate the performance. For the Hash Table, Searching an element is between O(1) and O(n) in the worst case. That means, that in the best case your access time is independant of the number of elements in your map but in the worst case it is linear dependant on the size of your hash table.
A Binary tree has a guaranteed search complexity of O(nlog(n)). That means, that searching an element always depends on the size of the array, but in the Worst Case its faster than a hash table.
You can look up some Big O Complexities at this handy website here: http://bigocheatsheet.com/
I have big C++/STL data structures (myStructType) with imbricated lists and maps. I have many objects of this type I want to LRU-cache with a key. I can reload objects from disk when needed. Moreover, it has to be shared in a multiprocessing high performance application running on a BSD plateform.
I can see several solutions:
I can consider a life-time sorted list of pair<size_t lifeTime, myStructType v> plus a map to o(1) access the index of the desired object in the list from its key, I can use shm and mmap to store everything, and a lock to manage access (cf here).
I can use a redis server configured for LRU, and redesign my data structures to redis key/value and key/lists pairs.
I can use a redis server configured for LRU, and serialise my data structures (myStructType) to have a simple key/value to manage with redis.
There may be other solutions of course. How would you do that, or better, how have you successfully done that, keeping in mind high performance ?
In addition, I would like to avoid heavy dependencies like Boost.
I actually built caches (not only LRU) recently.
Options 2 and 3 are quite likely not faster than re-reading from disk. That's effectively no cache at all. Also, this would be a far heavier dependency than Boost.
Option 1 can be challenging. For instance, you suggest "a lock". That would be quite a contended lock, as it must protect each and every lifetime update, plus all LRU operations. Since your objects are already heavy, it may be worthwhile to have a unique lock per object. There are intermediate variants of this solution, where there is more than one lock, but also more than one object per lock. (You still need a key to protect the whole map, but that's for replacement only)
You can also consider if you really need strict LRU. That strategy assumes that the chances of an object being reused decreases over time. If that's not actually true, random replacement is just as good. You can also consider evicting more than one element at a time. One of the challenges is that when an element needs removing, it would be so from all threads, but it's sufficient if one thread removes it. That's why a batch removal helps: if a thread tries to take a lock for batch removal and it fails, it can continue under the assumption that the cache will have free space soon.
One quick win is to not update the LRU time of the last used element. It was already the newest, making it any newer won't help. This of course only has an effect if you often use that element quickly again, but (as noted above) otherwise you'd just use random eviction.
I have the following requirements:
I need a datastructure with key,value pairs(keys are integers if that helps).
I need the below operations:-
Iteration(most used)
Insertion (2nd most used)
Searching by key and deletion(least)
I plan to use multiple locks over the structure for concurrent access.
What is the ideal data structure to use?
Map or an unordered map?
I think unordered map makes sense, because i can insert in O(1), delete in O(1). But i am not sure of the iteration. How bad is the performance when compared to map?
Also, i plan to use multiple locks on blocks instead of the whole structure. Any good implementation example of this?
Thanks
The speed of iterator incrementing is O(1) for both containers, although you might get somewhat better cache locality from std::unordered_map.
Apart from the slower O(log N) find/insert/erase functionality of std::map, one other difference is that std::map provides bidirectional iterators, whereas the faster (amortized O(1) element access) std::unordered_map only provides forward iterators.
The excellent book C++ Concurrency in Action: Practical Multithreading by Anthony Williams provides a code example of a multithreaded unordered_map with a lock per entry. This book is highly recommended if you are doing serious multithreaded coding.
Iteration is not a problem in an unordered_map. It is a little less efficient than a vector, but not largely so.
As always, you will need to benchmark for YOUR use-cases, and compare with other container types if it's a critical part of your code.
Not sure what you mean by "multiple locks on blocks instead of the whole structure" - any container updates will need to be locked for the whole container...
Why not simply use an existing concurrent_unordered_map which you can find in both TBB and Concrt.
Have you thought about trying a std::deque The reasoning being as follows:
Iteration is fast - data should be more or less packed close (unlike lists)
Insertion (at either end) should be quick - data in a deque is never resized
Iteration and deletion slow (but uncommon usecase).
If the last two cases are common, a std::list may be used. Also consider testing a std::vector` since it is more cache efficient.
Iteration in an unordered_map may be slow due to iterating over a large number of unused elements in the hashtable. Insertions will be quich until collision levels become intolerable at which point the whole data structure will need to laid out again.
maps have relatively fast iteration except that data elements may be far apart. Insertion can be slow due to the re-balancing of the red-black trees that this requires.
The main usecase for unordered_maps is for fast lookup (O1). normal map have fastish lookup (O log n) but much better iteration performance.
If you have hard real-time requirements, I would recommend map over unordered_map. std::map has guaranteed performance 100% of the time, but the std::unordered_map may do a rehash and completely ruin real-time performance in some critical corner case. In general, I prefer red-black trees (std::map) over hashtables (std::unordered_map) if I need absolute guarantees on worst-case performance.
I'm building a content storage system for my game engine and I'm looking at possible alternatives for storing the data. Since this is a game, it's obvious that performance is important. Especially considering various entities in the engine will be requesting resources from the data structures of the content manager upon their creation. I'd like to be able to search resources by a name instead of an index number, so a dictionary of some sort would be appropriate.
What are the pros and cons to using an std::map and to creating my own dictionary class based on std::vector? Are there any speed differences (if so, where will performance take a hit? I.e. appending vs. accessing) and is there any point in taking the time to writing my own class?
For some background on what needs to happen:
Writing to the data structures occurs only at one time, when the engine loads. So no writing actually occurs during gameplay. When the engine exits, these data structures are to be cleaned up. Reading from them can occur at any time, whenever an entity is created or a map is swapped. There can be as little as one entity being created at a time, or as many as 20, each needing a variable number of resources. Resource size can also vary depending on the size of the file being read in at the start of the engine, images being the smallest and music being the largest depending on the format (.ogg or .midi).
Map: std::map has guaranteed logarithmic lookup complexity. It's usually implemented by experts and will be of high quality (e.g. exception safety). You can use custom allocators for custom memory requirements.
Your solution: It'll be written by you. A vector is for contiguous storage with random access by position, so how will you implement lookup by value? Can you do it with guaranteed logarithmic complexity or better? Do you have specific memory requirements? Are you sure you can implement a the lookup algorithm correctly and efficiently?
3rd option: If you key type is string (or something that's expensive to compare), do also consider std::unordered_map, which has constant-time lookup by value in typical situations (but not quite guaranteed).
If you want the speed guarantee of std::map as well as the low memory usage of std::vector you could put your data in a std::vector, std::sort it and then use std::lower_bound to find the elements.
std::map is written with performance in mind anyway, whilst it does have some overhead as they have attempted to generalize to all circumstances, it will probably end up more efficient than your own implementation anyway. It uses a red-black binary tree, giving all of it's operations O[log n] efficiency (aside from copying and iterating for obvious reasons).
How often will you be reading/writing to the map, and how long will each element be in it? Also, you have to consider how often will you need to resize etc. Each of these questions is crucial to choosing the correct data structure for your implementation.
Overall, one of the std functions will probably be what you want, unless you need functionality which is not in a single one of them, or if you have an idea which could improve on their time complexities.
EDIT: Based on your update, I would agree with Kerrek SB that if you're using C++0x, then std::unordered_map would be a good data structure to use in this case. However, bear in mind that your performance can degrade to linear time complexity if you have conflicting hashes (this cannot happen with std::map), as it will store the two pair's in the same bucket. Whilst this is rare, the probability of it obviously increases with the number of elements. So if you're writing a huge game, it's possible that std::unordered_map could become less optimal than std::map. Just a consideration. :)