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 com.oltpbenchmark.distributions.ZipfianGenerator

        final PreparedStatement followersInsert = this.conn.prepareStatement(SQLUtil.getInsertSQL(catalog_tbl));


        int total = 1;
        int batchSize = 0;
        
        ZipfianGenerator zipfFollowee = new ZipfianGenerator(this.num_users,1.75);
        ZipfianGenerator zipfFollows = new ZipfianGenerator(this.num_follows,1.75);
        List<Integer> followees = new ArrayList<Integer>();
        for (int follower = 0; follower < this.num_users; follower++) {
            followees.clear();
            int time = zipfFollows.nextInt();
            if(time==0) time=1; // At least this follower will follow 1 user 
            for (int f = 0; f < time; ) {
                int followee = zipfFollowee.nextInt();
                if (follower != followee && !followees.contains(followee)) {
                    followsInsert.setInt(1, follower);

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Examples of com.oltpbenchmark.distributions.ZipfianGenerator

        assert (catalog_tbl != null);
        String sql = SQLUtil.getInsertSQL(catalog_tbl);
        PreparedStatement reviewInsert = this.conn.prepareStatement(sql);
        
        //
        ZipfianGenerator numReviews = new ZipfianGenerator(num_reviews, 1.8);
        ZipfianGenerator reviewer = new ZipfianGenerator(num_users);
        int total = 0;
        int batch = 0;
        for (int i = 0; i < num_items; i++) {
            List<Integer> reviewers = new ArrayList<Integer>();
            int review_count = numReviews.nextInt();
            if (review_count == 0)
                review_count = 1; // make sure at least each item has a review
            for (int rc = 0; rc < review_count;) {
                int u_id = reviewer.nextInt();
                if (!reviewers.contains(u_id)) {
                    rc++;
                    reviewInsert.setInt(1, total);
                    reviewInsert.setInt(2, u_id);
                    reviewInsert.setInt(3, i);

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Examples of com.oltpbenchmark.distributions.ZipfianGenerator

        PreparedStatement trustInsert = this.conn.prepareStatement(sql);
        
        //
        int total = 0;
        int batch = 0;
        ZipfianGenerator numTrust = new ZipfianGenerator(num_trust, 1.95);
        ScrambledZipfianGenerator reviewed = new ScrambledZipfianGenerator(num_users);
        Random isTrusted = new Random(System.currentTimeMillis());
        for (int i = 0; i < num_users; i++) {
            List<Integer> trusted = new ArrayList<Integer>();
            int trust_count = numTrust.nextInt();
            for (int tc = 0; tc < trust_count;) {
                int u_id = reviewed.nextInt();
                if (!trusted.contains(u_id)) {
                    tc++;
                    trustInsert.setInt(1, i);

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Examples of com.oltpbenchmark.distributions.ZipfianGenerator


    private final Map<Integer, String> m = new HashMap<Integer, String>();
    
    public YCSBWorker(int id, BenchmarkModule benchmarkModule, int init_record_count) {
        super(benchmarkModule, id);
        readRecord = new ZipfianGenerator(init_record_count);// pool for read keys
        randScan = new ZipfianGenerator(YCSBConstants.MAX_SCAN);
        
        synchronized (YCSBWorker.class) {
            // We must know where to start inserting
            if (insertRecord == null) {
                insertRecord = new CounterGenerator(init_record_count);

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Examples of com.yahoo.ycsb.generator.ZipfianGenerator

    if(fieldlengthdistribution.compareTo("constant") == 0) {
      fieldlengthgenerator = new ConstantIntegerGenerator(fieldlength);
    } else if(fieldlengthdistribution.compareTo("uniform") == 0) {
      fieldlengthgenerator = new UniformIntegerGenerator(1, fieldlength);
    } else if(fieldlengthdistribution.compareTo("zipfian") == 0) {
      fieldlengthgenerator = new ZipfianGenerator(1, fieldlength);
    } else if(fieldlengthdistribution.compareTo("histogram") == 0) {
      try {
        fieldlengthgenerator = new HistogramGenerator(fieldlengthhistogram);
      } catch(IOException e) {
        throw new WorkloadException("Couldn't read field length histogram file: "+fieldlengthhistogram, e);

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Examples of com.yahoo.ycsb.generator.ZipfianGenerator

    {
      scanlength=new UniformIntegerGenerator(1,maxscanlength);
    }
    else if (scanlengthdistrib.compareTo("zipfian")==0)
    {
      scanlength=new ZipfianGenerator(1,maxscanlength);
    }
    else
    {
      throw new WorkloadException("Distribution \""+scanlengthdistrib+"\" not allowed for scan length");
    }

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Examples of com.yahoo.ycsb.generator.ZipfianGenerator

        System.out.println("persisted");
    }


    private HashMap<String, ByteIterator> buildValues()
    {
        IntegerGenerator fieldlengthgenerator = new ZipfianGenerator(1, 4);
        HashMap<String, ByteIterator> values = new HashMap<String, ByteIterator>();


        for (int i = 0; i < 4; i++)
        {
            String fieldkey = "field" + i;
            ByteIterator data = new RandomByteIterator(fieldlengthgenerator.nextInt());
            values.put(fieldkey, data);
        }
        return values;
    }

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Examples of com.yahoo.ycsb.generator.ZipfianGenerator

    fieldchooser = new UniformIntegerGenerator(0, Config.getConfig().field_count - 1);


    if (Config.getConfig().scan_length_distribution.compareTo("uniform") == 0) {
      scanlength = new UniformIntegerGenerator(1, Config.getConfig().max_scan_length);
    } else if (Config.getConfig().scan_length_distribution.compareTo("zipfian") == 0) {
      scanlength = new ZipfianGenerator(1, Config.getConfig().max_scan_length);
    } else {
      throw new WorkloadException("Distribution \"" + Config.getConfig().scan_length_distribution
          + "\" not allowed for scan length");
    }
  }

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Examples of com.yahoo.ycsb.generator.ZipfianGenerator

    fieldchooser = new UniformIntegerGenerator(0, Config.getConfig().field_count - 1);


    if (Config.getConfig().scan_length_distribution.compareTo("uniform") == 0) {
      scanlength = new UniformIntegerGenerator(1, Config.getConfig().max_scan_length);
    } else if (Config.getConfig().scan_length_distribution.compareTo("zipfian") == 0) {
      scanlength = new ZipfianGenerator(1, Config.getConfig().max_scan_length);
    } else {
      throw new WorkloadException("Distribution \"" + Config.getConfig().scan_length_distribution + "\" not allowed for scan length");
    }
  }

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

        double data[] = new double[NUM_TRIALS];
        for (int trial = 0; trial < NUM_TRIALS; trial++) {
            long keys[][] = new long[3][NUM_KEYS];
            int num_reads = 0;


            ZipfianGenerator zipf = new ZipfianGenerator(NUM_TUPLES, ZIPF_CONSTANT);


            for (int i = 0; i < keys.length; i++) {
                for (int j = 0; j < keys[i].length; j++) {
//                    keys[i][j] = rand.nextInt(NUM_TUPLES);
                      keys[i][j] = zipf.nextInt(NUM_TUPLES); 
                    num_reads++;
                } // FOR
            } // FOR
            
            Procedure proc = this.getProcedure(SelectBlaster.class);

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