ALEX crashes in non-batch mode

[alihadian]

I ran ALEX against all of the SOSD benchmark's uint64 datasets (download link). I tried to insert the first 100M records, but ALEX crashed for all datasets.

When I insert the first few thousand records (e.g., 100K records), the code does not crash, but ALEX crashes for a larger number of insertions across all datasets. It's most likely a case that went unnoticed. The error message is as follows:

alex_nodes.h:540: int alex::AlexDataNode<T, P, Compare, Alloc, allow_duplicates>::num_keys_in_range(int, int) const [with T = long unsigned int; P = int; Compare = alex::AlexCompare; Alloc = std::allocator<std::pair<long unsigned int, int> >; bool allow_duplicates = true]: Assertion `left >= 0 && left < right && right <= data_capacity_' failed. Aborted (core dumped)

I have plenty of memory (50GB+) so it is certainly not a memory issue.

The code is simple. I just insert the records one by one, up to MAX_INSERTS

#include <iostream>
#include <vector>
#include <algorithm>
#include <time.h>

#include "alex.h"

#include "load.hpp"

using namespace std;

#define KEY_TYPE uint64_t
#define PAYLOAD_TYPE int

#define MAX_INSERTS 100000000

template <typename T>
void test_alex(vector<T> &data, string filename)
{
	alex::Alex<KEY_TYPE, PAYLOAD_TYPE> index;

	int cnt = 0;

	for (typename vector<T>::iterator it = data.begin(); it != data.end(); it++)
		if (cnt <= MAX_INSERTS)
			index.insert(*it, ++cnt);

	cout << cnt << endl;
}

int main(int argc, char **argv)
{
	string filename = argv[1];

	vector<uint64_t> data;
	load_bin<uint64_t>(filename, data);
	sort(data.begin(), data.end());
	data.erase(unique(data.begin(), data.end()), data.end());

	test_alex(data, filename);
}

[jialinding]

Thanks for bringing this our attention Ali. I'll look into it.

[jialinding]

The latest commit should fix the issues you're seeing. Let me know if it still doesn't work.

[liang2419]

Hi Jialin,

Thanks for your updates. We test the insertion of 100M with SOSD datasets (uint64), it works on fb, normal, osm, however, doesn't work for books, lognormal, uniform_dense, uniform_sparse. And the error message is the same as before.

[jialinding]

Thanks for pointing that out. It turns out the assert itself was too restrictive. The latest commit should now work on all uint64 datasets from SOSD.