Examples of ZipfianGenerator

com.oltpbenchmark.distributions.ZipfianGenerator
A generator of a zipfian distribution. It produces a sequence of items, such that some items are more popular than others, according to a zipfian distribution. When you construct an instance of this class, you specify the number of items in the set to draw from, either by specifying an itemcount (so that the sequence is of items from 0 to itemcount-1) or by specifying a min and a max (so that the sequence is of items from min to max inclusive). After you construct the instance, you can change the number of items by calling nextInt(itemcount) or nextLong(itemcount). Note that the popular items will be clustered together, e.g. item 0 is the most popular, item 1 the second most popular, and so on (or min is the most popular, min+1 the next most popular, etc.) If you don't want this clustering, and instead want the popular items scattered throughout the item space, then use ScrambledZipfianGenerator instead. Be aware: initializing this generator may take a long time if there are lots of items to choose from (e.g. over a minute for 100 million objects). This is because certain mathematical values need to be computed to properly generate a zipfian skew, and one of those values (zeta) is a sum sequence from 1 to n, where n is the itemcount. Note that if you increase the number of items in the set, we can compute a new zeta incrementally, so it should be fast unless you have added millions of items. However, if you decrease the number of items, we recompute zeta from scratch, so this can take a long time. The algorithm used here is from "Quickly Generating Billion-Record Synthetic Databases", Jim Gray et al, SIGMOD 1994.
com.yahoo.ycsb.generator.ZipfianGenerator
A generator of a zipfian distribution. It produces a sequence of items, such that some items are more popular than others, according to a zipfian distribution. When you construct an instance of this class, you specify the number of items in the set to draw from, either by specifying an itemcount (so that the sequence is of items from 0 to itemcount-1) or by specifying a min and a max (so that the sequence is of items from min to max inclusive). After you construct the instance, you can change the number of items by calling nextInt(itemcount) or nextLong(itemcount). Note that the popular items will be clustered together, e.g. item 0 is the most popular, item 1 the second most popular, and so on (or min is the most popular, min+1 the next most popular, etc.) If you don't want this clustering, and instead want the popular items scattered throughout the item space, then use ScrambledZipfianGenerator instead. Be aware: initializing this generator may take a long time if there are lots of items to choose from (e.g. over a minute for 100 million objects). This is because certain mathematical values need to be computed to properly generate a zipfian skew, and one of those values (zeta) is a sum sequence from 1 to n, where n is the itemcount. Note that if you increase the number of items in the set, we can compute a new zeta incrementally, so it should be fast unless you have added millions of items. However, if you decrease the number of items, we recompute zeta from scratch, so this can take a long time. The algorithm used here is from "Quickly Generating Billion-Record Synthetic Databases", Jim Gray et al, SIGMOD 1994.
edu.brown.benchmark.ycsb.distributions.ZipfianGenerator
A generator of a zipfian distribution. It produces a sequence of items, such that some items are more popular than others, according to a zipfian distribution. When you construct an instance of this class, you specify the number of items in the set to draw from, either by specifying an itemcount (so that the sequence is of items from 0 to itemcount-1) or by specifying a min and a max (so that the sequence is of items from min to max inclusive). After you construct the instance, you can change the number of items by calling nextInt(itemcount) or nextLong(itemcount). Note that the popular items will be clustered together, e.g. item 0 is the most popular, item 1 the second most popular, and so on (or min is the most popular, min+1 the next most popular, etc.) If you don't want this clustering, and instead want the popular items scattered throughout the item space, then use ScrambledZipfianGenerator instead. Be aware: initializing this generator may take a long time if there are lots of items to choose from (e.g. over a minute for 100 million objects). This is because certain mathematical values need to be computed to properly generate a zipfian skew, and one of those values (zeta) is a sum sequence from 1 to n, where n is the itemcount. Note that if you increase the number of items in the set, we can compute a new zeta incrementally, so it should be fast unless you have added millions of items. However, if you decrease the number of items, we recompute zeta from scratch, so this can take a long time. The algorithm used here is from "Quickly Generating Billion-Record Synthetic Databases", Jim Gray et al, SIGMOD 1994.

Examples of edu.brown.benchmark.ycsb.distributions.ZipfianGenerator

        }
        else {
            this.init_record_count = (long)Math.round(YCSBConstants.NUM_RECORDS * this.getScaleFactor());
        }
        this.rand_gen = new Random(); 
        this.randScan = new ZipfianGenerator(YCSBConstants.MAX_SCAN);
                
//        // initialize distribution generators 
//        // We must know where to start inserting
//        this.insertRecord = new CustomSkewGenerator(this.rand_gen, this.init_record_count, 
//                                            YCSBConstants.HOT_DATA_WORKLOAD_SKEW, YCSBConstants.HOT_DATA_SIZE, 
//                                            YCSBConstants.WARM_DATA_WORKLOAD_SKEW, YCSBConstants.WARM_DATA_SIZE);
//
//        this.readRecord = new CustomSkewGenerator(this.rand_gen, this.init_record_count, 
//                                            YCSBConstants.HOT_DATA_WORKLOAD_SKEW, YCSBConstants.HOT_DATA_SIZE, 
//                                            YCSBConstants.WARM_DATA_WORKLOAD_SKEW, YCSBConstants.WARM_DATA_SIZE);
        
        this.insertRecord = new ZipfianGenerator(this.init_record_count, this.skewFactor);
        this.readRecord = new ZipfianGenerator(this.init_record_count, this.skewFactor);
        
        // Initialize the sampling table
        Histogram<Transaction> txns = new ObjectHistogram<Transaction>(); 
        for (Transaction t : Transaction.values()) {
            Integer weight = this.getTransactionWeight(t.callName);

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Examples of edu.brown.benchmark.ycsb.distributions.ZipfianGenerator

        double sigma = 0.51d;
        
        int round = num_rounds;
        while (round-- > 0) {
//            RandomDistribution.Zipf zipf = new RandomDistribution.Zipf(this.rand, min, max, sigma);
            ZipfianGenerator zipf = new ZipfianGenerator(20, sigma);
            Histogram<Integer> hist = new ObjectHistogram<Integer>();
            System.out.println("Round #" + round + " [sigma=" + sigma + "]");
            for (int i = 0; i < num_records; i++) {
                int value = zipf.nextInt();
                hist.put(value);
            } // FOR
            Long last = null;
            for (Integer value : hist.values()) {
                long current = hist.get(value);

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