/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you 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
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package org.apache.beam.sdk.io.jdbc;

import static org.apache.beam.sdk.io.common.IOITHelper.executeWithRetry;
import static org.apache.beam.sdk.io.common.IOITHelper.readIOTestPipelineOptions;

import com.google.cloud.Timestamp;
import java.sql.SQLException;
import java.util.HashSet;
import java.util.List;
import java.util.Set;
import java.util.UUID;
import java.util.function.Function;
import org.apache.beam.sdk.PipelineResult;
import org.apache.beam.sdk.coders.SerializableCoder;
import org.apache.beam.sdk.io.GenerateSequence;
import org.apache.beam.sdk.io.common.DatabaseTestHelper;
import org.apache.beam.sdk.io.common.HashingFn;
import org.apache.beam.sdk.io.common.PostgresIOTestPipelineOptions;
import org.apache.beam.sdk.io.common.TestRow;
import org.apache.beam.sdk.testing.PAssert;
import org.apache.beam.sdk.testing.TestPipeline;
import org.apache.beam.sdk.testutils.NamedTestResult;
import org.apache.beam.sdk.testutils.metrics.IOITMetrics;
import org.apache.beam.sdk.testutils.metrics.MetricsReader;
import org.apache.beam.sdk.testutils.metrics.TimeMonitor;
import org.apache.beam.sdk.transforms.Combine;
import org.apache.beam.sdk.transforms.Count;
import org.apache.beam.sdk.transforms.ParDo;
import org.apache.beam.sdk.transforms.Top;
import org.apache.beam.sdk.values.PCollection;
import org.junit.AfterClass;
import org.junit.BeforeClass;
import org.junit.Rule;
import org.junit.Test;
import org.junit.runner.RunWith;
import org.junit.runners.JUnit4;
import org.postgresql.ds.PGSimpleDataSource;

/**
 * A test of {@link org.apache.beam.sdk.io.jdbc.JdbcIO} on an independent Postgres instance.
 *
 * <p>This test requires a running instance of Postgres. Pass in connection information using
 * PipelineOptions:
 *
 * <pre>
 *  ./gradlew integrationTest -p sdks/java/io/jdbc -DintegrationTestPipelineOptions='[
 *  "--postgresServerName=1.2.3.4",
 *  "--postgresUsername=postgres",
 *  "--postgresDatabaseName=myfancydb",
 *  "--postgresPassword=mypass",
 *  "--postgresSsl=false",
 *  "--numberOfRecords=1000" ]'
 *  --tests org.apache.beam.sdk.io.jdbc.JdbcIOIT
 *  -DintegrationTestRunner=direct
 * </pre>
 *
 * <p>Please see 'build_rules.gradle' file for instructions regarding running this test using Beam
 * performance testing framework.
 */
@RunWith(JUnit4.class)
public class JdbcIOIT {

  private static final String NAMESPACE = JdbcIOIT.class.getName();
  private static int numberOfRows;
  private static PGSimpleDataSource dataSource;
  private static String tableName;
  private static String bigQueryDataset;
  private static String bigQueryTable;
  @Rule public TestPipeline pipelineWrite = TestPipeline.create();
  @Rule public TestPipeline pipelineRead = TestPipeline.create();

  @BeforeClass
  public static void setup() throws Exception {
    PostgresIOTestPipelineOptions options =
        readIOTestPipelineOptions(PostgresIOTestPipelineOptions.class);

    bigQueryDataset = options.getBigQueryDataset();
    bigQueryTable = options.getBigQueryTable();
    numberOfRows = options.getNumberOfRecords();
    dataSource = DatabaseTestHelper.getPostgresDataSource(options);
    tableName = DatabaseTestHelper.getTestTableName("IT");
    executeWithRetry(JdbcIOIT::createTable);
  }

  private static void createTable() throws SQLException {
    DatabaseTestHelper.createTable(dataSource, tableName);
  }

  @AfterClass
  public static void tearDown() throws Exception {
    executeWithRetry(JdbcIOIT::deleteTable);
  }

  private static void deleteTable() throws SQLException {
    DatabaseTestHelper.deleteTable(dataSource, tableName);
  }

  /** Tests writing then reading data for a postgres database. */
  @Test
  public void testWriteThenRead() {
    PipelineResult writeResult = runWrite();
    writeResult.waitUntilFinish();
    PipelineResult readResult = runRead();
    readResult.waitUntilFinish();
    gatherAndPublishMetrics(writeResult, readResult);
  }

  private void gatherAndPublishMetrics(PipelineResult writeResult, PipelineResult readResult) {
    String uuid = UUID.randomUUID().toString();
    String timestamp = Timestamp.now().toString();

    Set<Function<MetricsReader, NamedTestResult>> metricSuppliers =
        getWriteMetricSuppliers(uuid, timestamp);
    IOITMetrics writeMetrics =
        new IOITMetrics(metricSuppliers, writeResult, NAMESPACE, uuid, timestamp);
    writeMetrics.publish(bigQueryDataset, bigQueryTable);

    IOITMetrics readMetrics =
        new IOITMetrics(
            getReadMetricSuppliers(uuid, timestamp), readResult, NAMESPACE, uuid, timestamp);
    readMetrics.publish(bigQueryDataset, bigQueryTable);
  }

  private Set<Function<MetricsReader, NamedTestResult>> getWriteMetricSuppliers(
      String uuid, String timestamp) {
    Set<Function<MetricsReader, NamedTestResult>> suppliers = new HashSet<>();
    suppliers.add(
        reader -> {
          long writeStart = reader.getStartTimeMetric("write_time");
          long writeEnd = reader.getEndTimeMetric("write_time");
          return NamedTestResult.create(
              uuid, timestamp, "write_time", (writeEnd - writeStart) / 1e3);
        });
    return suppliers;
  }

  private Set<Function<MetricsReader, NamedTestResult>> getReadMetricSuppliers(
      String uuid, String timestamp) {
    Set<Function<MetricsReader, NamedTestResult>> suppliers = new HashSet<>();
    suppliers.add(
        reader -> {
          long readStart = reader.getStartTimeMetric("read_time");
          long readEnd = reader.getEndTimeMetric("read_time");
          return NamedTestResult.create(uuid, timestamp, "read_time", (readEnd - readStart) / 1e3);
        });
    return suppliers;
  }

  /**
   * Writes the test dataset to postgres.
   *
   * <p>This method does not attempt to validate the data - we do so in the read test. This does
   * make it harder to tell whether a test failed in the write or read phase, but the tests are much
   * easier to maintain (don't need any separate code to write test data for read tests to the
   * database.)
   */
  private PipelineResult runWrite() {
    pipelineWrite
        .apply(GenerateSequence.from(0).to(numberOfRows))
        .apply(ParDo.of(new TestRow.DeterministicallyConstructTestRowFn()))
        .apply(ParDo.of(new TimeMonitor<>(NAMESPACE, "write_time")))
        .apply(
            JdbcIO.<TestRow>write()
                .withDataSourceConfiguration(JdbcIO.DataSourceConfiguration.create(dataSource))
                .withStatement(String.format("insert into %s values(?, ?)", tableName))
                .withPreparedStatementSetter(new JdbcTestHelper.PrepareStatementFromTestRow()));

    return pipelineWrite.run();
  }

  /**
   * Read the test dataset from postgres and validate its contents.
   *
   * <p>When doing the validation, we wish to ensure that we: 1. Ensure *all* the rows are correct
   * 2. Provide enough information in assertions such that it is easy to spot obvious errors (e.g.
   * all elements have a similar mistake, or "only 5 elements were generated" and the user wants to
   * see what the problem was.
   *
   * <p>We do not wish to generate and compare all of the expected values, so this method uses
   * hashing to ensure that all expected data is present. However, hashing does not provide easy
   * debugging information (failures like "every element was empty string" are hard to see), so we
   * also: 1. Generate expected values for the first and last 500 rows 2. Use containsInAnyOrder to
   * verify that their values are correct. Where first/last 500 rows is determined by the fact that
   * we know all rows have a unique id - we can use the natural ordering of that key.
   */
  private PipelineResult runRead() {
    PCollection<TestRow> namesAndIds =
        pipelineRead
            .apply(
                JdbcIO.<TestRow>read()
                    .withDataSourceConfiguration(JdbcIO.DataSourceConfiguration.create(dataSource))
                    .withQuery(String.format("select name,id from %s;", tableName))
                    .withRowMapper(new JdbcTestHelper.CreateTestRowOfNameAndId())
                    .withCoder(SerializableCoder.of(TestRow.class)))
            .apply(ParDo.of(new TimeMonitor<>(NAMESPACE, "read_time")));

    PAssert.thatSingleton(namesAndIds.apply("Count All", Count.globally()))
        .isEqualTo((long) numberOfRows);

    PCollection<String> consolidatedHashcode =
        namesAndIds
            .apply(ParDo.of(new TestRow.SelectNameFn()))
            .apply("Hash row contents", Combine.globally(new HashingFn()).withoutDefaults());
    PAssert.that(consolidatedHashcode)
        .containsInAnyOrder(TestRow.getExpectedHashForRowCount(numberOfRows));

    PCollection<List<TestRow>> frontOfList = namesAndIds.apply(Top.smallest(500));
    Iterable<TestRow> expectedFrontOfList = TestRow.getExpectedValues(0, 500);
    PAssert.thatSingletonIterable(frontOfList).containsInAnyOrder(expectedFrontOfList);

    PCollection<List<TestRow>> backOfList = namesAndIds.apply(Top.largest(500));
    Iterable<TestRow> expectedBackOfList =
        TestRow.getExpectedValues(numberOfRows - 500, numberOfRows);
    PAssert.thatSingletonIterable(backOfList).containsInAnyOrder(expectedBackOfList);

    return pipelineRead.run();
  }
}
