[GitHub] [incubator-hudi] yanghua commented on a change in pull request #1511: [HUDI-789]Adjust logic of upsert in HDFSParquetImporter

[GitBox <gi...@apache.org>]

yanghua commented on a change in pull request #1511: [HUDI-789]Adjust logic of upsert in HDFSParquetImporter
URL: https://github.com/apache/incubator-hudi/pull/1511#discussion_r409395668
 
 

 ##########
 File path: hudi-utilities/src/test/java/org/apache/hudi/utilities/TestHDFSParquetImporter.java
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 @@ -157,7 +176,103 @@ protected int dataImport(JavaSparkContext jsc) throws IOException {
     }
   }
 
-  private void createRecords(Path srcFolder) throws ParseException, IOException {
+  private void insert(JavaSparkContext jsc) throws IOException, ParseException {
+    // Create schema file.
+    String schemaFile = new Path(basePath, "file.schema").toString();
+    createSchemaFile(schemaFile);
+
+    HDFSParquetImporter.Config cfg = getHDFSParquetImporterConfig(srcFolder.toString(), hoodieFolder.toString(),
+        "testTable", "COPY_ON_WRITE", "_row_key", "timestamp", 1, schemaFile);
+    HDFSParquetImporter dataImporter = new HDFSParquetImporter(cfg);
+
+    dataImporter.dataImport(jsc, 0);
+  }
+
+  /**
+   * Test successful insert and verify data consistency.
+   */
+  @Test
+  public void testImportInsert() throws IOException, ParseException {
+    JavaSparkContext jsc = null;
+    try {
+      jsc = getJavaSparkContext();
+      insert(jsc);
+      SQLContext sqlContext = new SQLContext(jsc);
+      Dataset<Row> ds = HoodieClientTestUtils.read(jsc, basePath + "/testTarget", sqlContext, dfs, basePath + "/testTarget/*/*/*/*");
+
+      List<Row> readData = ds.select("timestamp", "_row_key", "rider", "driver", "begin_lat", "begin_lon", "end_lat", "end_lon").collectAsList();
+      List<HoodieModel> result = readData.stream().map(row ->
+          new HoodieModel(row.getDouble(0), row.getString(1), row.getString(2), row.getString(3), row.getDouble(4),
+              row.getDouble(5), row.getDouble(6), row.getDouble(7)))
+          .collect(Collectors.toList());
+
+      List<HoodieModel> expected = insertData.stream().map(g ->
+          new HoodieModel(Double.valueOf(g.get("timestamp").toString()), g.get("_row_key").toString(), g.get("rider").toString(), g.get("driver").toString(),
+              Double.valueOf(g.get("begin_lat").toString()), Double.valueOf(g.get("begin_lon").toString()), Double.valueOf(g.get("end_lat").toString()),
+              Double.valueOf(g.get("end_lon").toString())))
+          .collect(Collectors.toList());
+
+      assertTrue(result.containsAll(expected) && expected.containsAll(result) && result.size() == expected.size());
+    } finally {
+      if (jsc != null) {
+        jsc.stop();
+      }
+    }
+  }
+
+  /**
+   * Test upsert data and verify data consistency.
+   */
+  @Test
+  public void testImportWithUpsert() throws IOException, ParseException {
+    JavaSparkContext jsc = null;
+    try {
+      jsc = getJavaSparkContext();
+      insert(jsc);
+
+      // Create schema file.
+      String schemaFile = new Path(basePath, "file.schema").toString();
+
+      Path upsertFolder = new Path(basePath, "testUpsertSrc");
+      List<GenericRecord> upsertData = createUpsertRecords(upsertFolder);
+
+      HDFSParquetImporter.Config cfg = getHDFSParquetImporterConfig(upsertFolder.toString(), hoodieFolder.toString(),
+          "testTable", "COPY_ON_WRITE", "_row_key", "timestamp", 1, schemaFile);
+      cfg.command = "upsert";
+      HDFSParquetImporter dataImporter = new HDFSParquetImporter(cfg);
+
+      dataImporter.dataImport(jsc, 0);
+
+      // construct result, remove top 10 and add upsert data.
+      List<GenericRecord> expectData = insertData.subList(11, 96);
+      expectData.addAll(upsertData);
+
+      // read latest data
+      SQLContext sqlContext = new SQLContext(jsc);
+      Dataset<Row> ds = HoodieClientTestUtils.read(jsc, basePath + "/testTarget", sqlContext, dfs, basePath + "/testTarget/*/*/*/*");
+
+      List<Row> readData = ds.select("timestamp", "_row_key", "rider", "driver", "begin_lat", "begin_lon", "end_lat", "end_lon").collectAsList();
+      List<HoodieModel> result = readData.stream().map(row ->
+          new HoodieModel(row.getDouble(0), row.getString(1), row.getString(2), row.getString(3), row.getDouble(4),
+              row.getDouble(5), row.getDouble(6), row.getDouble(7)))
+          .collect(Collectors.toList());
+
+      // get expected result
+      List<HoodieModel> expected = expectData.stream().map(g ->
+          new HoodieModel(Double.valueOf(g.get("timestamp").toString()), g.get("_row_key").toString(), g.get("rider").toString(), g.get("driver").toString(),
+              Double.valueOf(g.get("begin_lat").toString()), Double.valueOf(g.get("begin_lon").toString()), Double.valueOf(g.get("end_lat").toString()),
 
 Review comment:
   I would suggest one field one line for so many arguments.

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