[FSTORE-414] feature_view.create_train_test_split returns empty df

java/src/main/java/com/logicalclocks/hsfs/engine/SparkEngine.java

@@ -49,6 +49,7 @@
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SaveMode;
 import org.apache.spark.sql.SparkSession;
+import org.apache.spark.sql.functions;
 import org.apache.spark.sql.streaming.DataStreamReader;
 import org.apache.spark.sql.streaming.DataStreamWriter;
 import org.apache.spark.sql.streaming.StreamingQuery;
@@ -57,6 +58,7 @@
 import org.apache.spark.sql.types.BinaryType;
 import org.apache.spark.sql.types.BooleanType;
 import org.apache.spark.sql.types.ByteType;
+import org.apache.spark.sql.types.DataTypes;
 import org.apache.spark.sql.types.DateType;
 import org.apache.spark.sql.types.DecimalType;
 import org.apache.spark.sql.types.DoubleType;
@@ -303,27 +305,43 @@ private Dataset<Row>[] timeSeriesSplit(TrainingDataset trainingDataset, Query qu
     int i = 0;
     for (Split split : splits) {
       if (dataset.count() > 0) {
+        String eventTime = query.getLeftFeatureGroup().getEventTime();
         String eventTimeType =
-            query.getLeftFeatureGroup().getFeature(query.getLeftFeatureGroup().getEventTime()).getType();
+            query.getLeftFeatureGroup().getFeature(eventTime).getType();
+
         if (BIGINT.getType().equals(eventTimeType)) {
+          String tmpEventTime = eventTime + "_hopsworks_tmp";
+          sparkSession.sqlContext()
+              .udf()
+              .register("checkEpochUDF", (Long input) -> {
+                if (Long.toString(input).length() > 10) {
+                  input = input / 1000;
+                  return input.longValue();
+                } else {
+                  return input;
+                }
+              }, DataTypes.LongType);
+          dataset = dataset.withColumn(tmpEventTime,functions.callUDF(
+              "checkEpochUDF", dataset.col(eventTime)));
+
           // event time in second. `getTime()` return in millisecond.
           datasetSplits[i] = dataset.filter(
               String.format(
                   "%d/1000 <= `%s` and `%s` < %d/1000",
                   split.getStartTime().getTime(),
-                  query.getLeftFeatureGroup().getEventTime(),
-                  query.getLeftFeatureGroup().getEventTime(),
+                  tmpEventTime,
+                  tmpEventTime,
                   split.getEndTime().getTime()
               )
-          );
+          ).drop(tmpEventTime);
         } else if (DATE.getType().equals(eventTimeType) || TIMESTAMP.getType().equals(eventTimeType)) {
           // unix_timestamp return in second. `getTime()` return in millisecond.
           datasetSplits[i] = dataset.filter(
               String.format(
                   "%d/1000 <= unix_timestamp(`%s`) and unix_timestamp(`%s`) < %d/1000",
                   split.getStartTime().getTime(),
-                  query.getLeftFeatureGroup().getEventTime(),
-                  query.getLeftFeatureGroup().getEventTime(),
+                  eventTime,
+                  eventTime,
                   split.getEndTime().getTime()
               )
           );

python/hsfs/engine/spark.py

@@ -39,7 +39,7 @@
         concat,
         col,
         from_json,
-        unix_timestamp,
+        udf,
     )
     from pyspark.sql.avro.functions import from_avro, to_avro
     from pyspark.sql.types import (
@@ -492,14 +492,13 @@ def _random_split(self, dataset, training_dataset):
     def _time_series_split(
         self, training_dataset, dataset, event_time, drop_event_time=False
     ):
-        result_dfs = {}
-        ts_type = dataset.select(event_time).dtypes[0][1]
-        ts_col = (
-            unix_timestamp(col(event_time)) * 1000
-            if ts_type in ["date", "timestamp"]
-            # jdbc supports timestamp precision up to second only.
-            else col(event_time) * 1000
+        # registering the UDF
+        _convert_event_time_to_timestamp = udf(
+            util.convert_event_time_to_timestamp, LongType()
         )
+
+        result_dfs = {}
+        ts_col = _convert_event_time_to_timestamp(col(event_time))
         for split in training_dataset.splits:
             result_df = dataset.filter(ts_col >= split.start_time).filter(
                 ts_col < split.end_time

python/hsfs/feature_view.py

@@ -190,9 +190,9 @@ def get_batch_query(
 
         # Arguments
             start_time: Start event time for the batch query. Optional. Strings should be formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`,
-                `%Y-%m-%d %H:%M:%S`, or `%Y-%m-%d %H:%M:%S.%f`.
+                `%Y-%m-%d %H:%M:%S`, or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             end_time: End event time for the batch query. Optional. Strings should be formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`,
-                `%Y-%m-%d %H:%M:%S`, or `%Y-%m-%d %H:%M:%S.%f`.
+                `%Y-%m-%d %H:%M:%S`, or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
 
         # Returns
             `str`: batch query
@@ -272,10 +272,10 @@ def get_batch_data(
         # Arguments
             start_time: Start event time for the batch query. Optional. Strings should be
                 formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             end_time: End event time for the batch query. Optional. Strings should be
                 formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             read_options: User provided read options. Defaults to `{}`.
         """
 
@@ -336,10 +336,10 @@ def create_training_data(
         # Arguments
             start_time: Start event time for the training dataset query. Optional. Strings should
                 be formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             end_time: End event time for the training dataset query. Optional. Strings should
                 be formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             storage_connector: Storage connector defining the sink location for the
                 training dataset, defaults to `None`, and materializes training dataset
                 on HopsFS.
@@ -444,16 +444,16 @@ def create_train_test_split(
             test_size: size of test set.
             train_start: Start event time for the train split query. Strings should
                 be formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             train_end: End event time for the train split query. Strings should
                 be formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             test_start: Start event time for the test split query. Strings should
                 be formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             test_end: End event time for the test split query. Strings should
                 be  formatted in one of the following ormats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             storage_connector: Storage connector defining the sink location for the
                 training dataset, defaults to `None`, and materializes training dataset
                 on HopsFS.
@@ -570,22 +570,22 @@ def create_train_validation_test_split(
             test_size: size of test set.
             train_start: Start event time for the train split query. Strings should
                 be formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             train_end: End event time for the train split query. Strings should
                 be formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             validation_start: Start event time for the validation split query. Strings
                 should be formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             validation_end: End event time for the validation split query. Strings
                 should be formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             test_start: Start event time for the test split query. Strings should
                 be formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             test_end: End event time for the test split query. Strings should
                 be formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             storage_connector: Storage connector defining the sink location for the
                 training dataset, defaults to `None`, and materializes training dataset
                 on HopsFS.
@@ -718,11 +718,11 @@ def training_data(
             start_time: Start event time for the training dataset query. Strings should
             be formatted in one of the following
                 formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             end_time: End event time for the training dataset query. Strings should be
             formatted in one of the following
                 formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             description: A string describing the contents of the training dataset to
                 improve discoverability for Data Scientists, defaults to empty string
                 `""`.
@@ -794,13 +794,13 @@ def train_test_split(
                 or `%Y-%m-%d %H:%M:%S.%f`.
             train_end: End event time for the train split query. Strings should
                 be formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             test_start: Start event time for the test split query. Strings should
                 be formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             test_end: End event time for the test split query. Strings should
                 be formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             description: A string describing the contents of the training dataset to
                 improve discoverability for Data Scientists, defaults to empty string
                 `""`.
@@ -893,22 +893,22 @@ def train_validation_test_split(
             test_size: size of test set. Should be between 0 and 1.
             train_start: Start event time for the train split query. Strings should
                 be formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             train_end: End event time for the train split query. Strings should
                 be formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             validation_start: Start event time for the validation split query. Strings
                 should be formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             validation_end: End event time for the validation split query. Strings
                 should be formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             test_start: Start event time for the test split query. Strings should
                 be formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             test_end: End event time for the test split query. Strings should
                 be formatted in one of the following formats `%Y-%m-%d`, `%Y-%m-%d %H`, `%Y-%m-%d %H:%M`, `%Y-%m-%d %H:%M:%S`,
-                or `%Y-%m-%d %H:%M:%S.%f`.
+                or `%Y-%m-%d %H:%M:%S.%f`. Int, i.e Unix Epoch should be in seconds.
             description: A string describing the contents of the training dataset to
                 improve discoverability for Data Scientists, defaults to empty string
                 `""`.

python/hsfs/training_dataset.py

@@ -238,8 +238,8 @@ def _append_time_split(
                 TrainingDatasetSplit(
                     name=split_name,
                     split_type=TrainingDatasetSplit.TIME_SERIES_SPLIT,
-                    start_time=util.convert_event_time_to_timestamp(start_time),
-                    end_time=util.convert_event_time_to_timestamp(end_time),
+                    start_time=start_time,
+                    end_time=end_time,
                 )
             )
 

python/hsfs/util.py

@@ -180,7 +180,7 @@ def convert_event_time_to_timestamp(event_time):
         if event_time == 0:
             raise ValueError("Event time should be greater than 0.")
         # jdbc supports timestamp precision up to second only.
-        if len(str(event_time)) < 13:
+        if len(str(event_time)) <= 10:
             event_time = event_time * 1000
         return event_time
     else:

python/tests/core/test_feature_view_engine.py

@@ -134,7 +134,9 @@ def test_save_time_travel_query(self, mocker):
         )
 
         fv = feature_view.FeatureView(
-            name="fv_name", query=query.as_of(1000), featurestore_id=feature_store_id
+            name="fv_name",
+            query=query.as_of(1000000000),
+            featurestore_id=feature_store_id,
         )
 
         # Act
@@ -449,7 +451,10 @@ def test_get_batch_query(self, mocker):
 
         # Act
         fv_engine.get_batch_query(
-            feature_view_obj=fv, start_time=1, end_time=2, with_label=False
+            feature_view_obj=fv,
+            start_time=1000000000,
+            end_time=2000000000,
+            with_label=False,
         )
 
         # Assert
@@ -486,7 +491,7 @@ def test_get_batch_query_string(self, mocker):
 
         # Act
         result = fv_engine.get_batch_query_string(
-            feature_view_obj=fv, start_time=1, end_time=2
+            feature_view_obj=fv, start_time=1000000000, end_time=2000000000
         )
 
         # Assert
@@ -526,7 +531,7 @@ def test_get_batch_query_string_pit_query(self, mocker):
 
         # Act
         result = fv_engine.get_batch_query_string(
-            feature_view_obj=fv, start_time=1, end_time=2
+            feature_view_obj=fv, start_time=1000000000, end_time=2000000000
         )
 
         # Assert