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Renato Marroquin renato2099

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import time
import pandas as pd
import numpy as np
import grizzly.grizzly as gr
verbosity = False
#data_path = "/data/0.tsv"
#edges = pd.read_csv(data_path, delimiter='\t', encoding='utf-8', header=None, dtype={'0': np.int64, '1': np.int64}, index_col=None)
#links = list(zip(edges[0], edges[1]))
links = [(1, 2),(1, 3),(1, 4),(2, 1),(2, 3),(3, 2)]
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
import me.prettyprint.cassandra.serializers.ObjectSerializer;
import me.prettyprint.cassandra.service.ThriftCfDef;
import me.prettyprint.hector.api.*;
import me.prettyprint.hector.api.beans.HColumn;
import me.prettyprint.hector.api.ddl.*;
import me.prettyprint.hector.api.factory.HFactory;
import me.prettyprint.hector.api.mutation.Mutator;
import me.prettyprint.hector.api.query.QueryResult;
import org.slf4j.*;
View nd4jMapTest.scala
import org.scalatest.FunSuite
import org.nd4j.api.Implicits._
import org.nd4j.linalg.api.ndarray.INDArray
import org.nd4j.linalg.factory.Nd4j
import scala.language.implicitConversions
class Main$Nd4jPerformanceTest extends FunSuite {
test("") {

Presto connector development 1

One of the very good design decisions Presto designers made is that it's loosely coupled from storages.

Presto is a distributed SQL executor engine, and doesn't manager schema or metadata of tables by itself. It doesn't manage read data from storage by itself. Those businesses are done by plugins called Connector. Presto comes with Hive connector built-in, which connects Hive's metastore and HDFS to Presto.

We can connect any storages into Presto by writing connector plugins.

Plugin Architecture

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