[HUDI-595] code cleanup, refactoring code out of PR# 1159 (#1302)

hudi-utilities/src/main/java/org/apache/hudi/utilities/HDFSParquetImporter.java

@@ -260,14 +260,13 @@ public class HDFSParquetImporter implements Serializable {
     public int parallelism = 1;
     @Parameter(names = {"--schema-file", "-sf"}, description = "path for Avro schema file", required = true)
     public String schemaFile = null;
-    @Parameter(names = {"--format", "-f"}, description = "Format for the input data.", required = false,
-        validateValueWith = FormatValidator.class)
+    @Parameter(names = {"--format", "-f"}, description = "Format for the input data.", validateValueWith = FormatValidator.class)
     public String format = null;
-    @Parameter(names = {"--spark-master", "-ms"}, description = "Spark master", required = false)
+    @Parameter(names = {"--spark-master", "-ms"}, description = "Spark master")
     public String sparkMaster = null;
     @Parameter(names = {"--spark-memory", "-sm"}, description = "spark memory to use", required = true)
     public String sparkMemory = null;
-    @Parameter(names = {"--retry", "-rt"}, description = "number of retries", required = false)
+    @Parameter(names = {"--retry", "-rt"}, description = "number of retries")
     public int retry = 0;
     @Parameter(names = {"--props"}, description = "path to properties file on localfs or dfs, with configurations for "
         + "hoodie client for importing")

hudi-utilities/src/main/java/org/apache/hudi/utilities/perf/TimelineServerPerf.java

@@ -114,7 +114,7 @@ public class TimelineServerPerf implements Serializable {
     d2.close();
 
     System.out.println("\n\n\nDumping all File Slices");
-    selected.stream().forEach(p -> fsView.getAllFileSlices(p).forEach(s -> System.out.println("\tMyFileSlice=" + s)));
+    selected.forEach(p -> fsView.getAllFileSlices(p).forEach(s -> System.out.println("\tMyFileSlice=" + s)));
 
     // Waiting for curl queries
     if (!useExternalTimelineServer && cfg.waitForManualQueries) {
@@ -131,17 +131,16 @@ public class TimelineServerPerf implements Serializable {
 
   public List<PerfStats> runLookups(JavaSparkContext jsc, List<String> partitionPaths, SyncableFileSystemView fsView,
       int numIterations, int concurrency) {
-    List<PerfStats> perfStats = jsc.parallelize(partitionPaths, cfg.numExecutors).flatMap(p -> {
+    return jsc.parallelize(partitionPaths, cfg.numExecutors).flatMap(p -> {
       ScheduledThreadPoolExecutor executor = new ScheduledThreadPoolExecutor(100);
       final List<PerfStats> result = new ArrayList<>();
       final List<ScheduledFuture<PerfStats>> futures = new ArrayList<>();
       List<FileSlice> slices = fsView.getLatestFileSlices(p).collect(Collectors.toList());
       String fileId = slices.isEmpty() ? "dummyId"
           : slices.get(new Random(Double.doubleToLongBits(Math.random())).nextInt(slices.size())).getFileId();
-      IntStream.range(0, concurrency).forEach(i -> {
-        futures.add(executor.schedule(() -> runOneRound(fsView, p, fileId, i, numIterations), 0, TimeUnit.NANOSECONDS));
-      });
-      futures.stream().forEach(x -> {
+      IntStream.range(0, concurrency).forEach(i -> futures.add(executor.schedule(() -> runOneRound(fsView, p, fileId,
+              i, numIterations), 0, TimeUnit.NANOSECONDS)));
+      futures.forEach(x -> {
         try {
           result.add(x.get());
         } catch (InterruptedException | ExecutionException e) {
@@ -149,12 +148,9 @@ public class TimelineServerPerf implements Serializable {
         }
       });
       System.out.println("SLICES are=");
-      slices.stream().forEach(s -> {
-        System.out.println("\t\tFileSlice=" + s);
-      });
+      slices.forEach(s -> System.out.println("\t\tFileSlice=" + s));
       return result.iterator();
     }).collect();
-    return perfStats;
   }
 
   private static PerfStats runOneRound(SyncableFileSystemView fsView, String partition, String fileId, int id,
@@ -194,7 +190,7 @@ public class TimelineServerPerf implements Serializable {
     }
 
     public void dump(List<PerfStats> stats) {
-      stats.stream().forEach(x -> {
+      stats.forEach(x -> {
         String row = String.format("%s,%d,%d,%d,%f,%f,%f,%f\n", x.partition, x.id, x.minTime, x.maxTime, x.meanTime,
             x.medianTime, x.p75, x.p95);
         System.out.println(row);
@@ -260,7 +256,7 @@ public class TimelineServerPerf implements Serializable {
     @Parameter(names = {"--num-iterations", "-i"}, description = "Number of iterations for each partitions")
     public Integer numIterations = 10;
 
-    @Parameter(names = {"--spark-master", "-ms"}, description = "Spark master", required = false)
+    @Parameter(names = {"--spark-master", "-ms"}, description = "Spark master")
     public String sparkMaster = "local[2]";
 
     @Parameter(names = {"--server-port", "-p"}, description = " Server Port")

hudi-utilities/src/main/java/org/apache/hudi/utilities/sources/helpers/IncrSourceHelper.java

@@ -38,7 +38,7 @@ public class IncrSourceHelper {
   private static String getStrictlyLowerTimestamp(String timestamp) {
     long ts = Long.parseLong(timestamp);
     Preconditions.checkArgument(ts > 0, "Timestamp must be positive");
-    Long lower = ts - 1;
+    long lower = ts - 1;
     return "" + lower;
   }
 
@@ -73,7 +73,7 @@ public class IncrSourceHelper {
 
     Option<HoodieInstant> nthInstant = Option.fromJavaOptional(activeCommitTimeline
         .findInstantsAfter(beginInstantTime, numInstantsPerFetch).getInstants().reduce((x, y) -> y));
-    return Pair.of(beginInstantTime, nthInstant.map(instant -> instant.getTimestamp()).orElse(beginInstantTime));
+    return Pair.of(beginInstantTime, nthInstant.map(HoodieInstant::getTimestamp).orElse(beginInstantTime));
   }
 
   /**

hudi-utilities/src/main/java/org/apache/hudi/utilities/sources/helpers/KafkaOffsetGen.java

@@ -94,8 +94,7 @@ public class KafkaOffsetGen {
 
       // Create initial offset ranges for each 'to' partition, with from = to offsets.
       OffsetRange[] ranges = new OffsetRange[toOffsetMap.size()];
-      toOffsetMap.entrySet().stream().map(e -> {
-        TopicPartition tp = e.getKey();
+      toOffsetMap.keySet().stream().map(tp -> {
         long fromOffset = fromOffsetMap.getOrDefault(tp, 0L);
         return OffsetRange.create(tp, fromOffset, fromOffset);
       }).sorted(byPartition).collect(Collectors.toList()).toArray(ranges);
@@ -208,9 +207,7 @@ public class KafkaOffsetGen {
     maxEventsToReadFromKafka = (maxEventsToReadFromKafka == Long.MAX_VALUE || maxEventsToReadFromKafka == Integer.MAX_VALUE)
         ? Config.maxEventsFromKafkaSource : maxEventsToReadFromKafka;
     long numEvents = sourceLimit == Long.MAX_VALUE ? maxEventsToReadFromKafka : sourceLimit;
-    OffsetRange[] offsetRanges = CheckpointUtils.computeOffsetRanges(fromOffsets, toOffsets, numEvents);
-
-    return offsetRanges;
+    return CheckpointUtils.computeOffsetRanges(fromOffsets, toOffsets, numEvents);
   }
 
   // check up checkpoint offsets is valid or not, if true, return checkpoint offsets,