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Posted to commits@hive.apache.org by mm...@apache.org on 2016/04/10 08:59:43 UTC
[11/12] hive git commit: HIVE-13111: Fix timestamp /
interval_day_time wrong results with HIVE-9862 (Matt McCline,
reviewed by Jason Dere)
http://git-wip-us.apache.org/repos/asf/hive/blob/ca11c393/itests/src/test/resources/testconfiguration.properties.orig
----------------------------------------------------------------------
diff --git a/itests/src/test/resources/testconfiguration.properties.orig b/itests/src/test/resources/testconfiguration.properties.orig
new file mode 100644
index 0000000..31cfb5b
--- /dev/null
+++ b/itests/src/test/resources/testconfiguration.properties.orig
@@ -0,0 +1,1327 @@
+# NOTE: files should be listed in alphabetical order
+minimr.query.files=auto_sortmerge_join_16.q,\
+ bucket4.q,\
+ bucket5.q,\
+ bucket6.q,\
+ bucket_many.q,\
+ bucket_num_reducers.q,\
+ bucket_num_reducers2.q,\
+ bucketizedhiveinputformat.q,\
+ bucketmapjoin6.q,\
+ bucketmapjoin7.q,\
+ constprog_partitioner.q,\
+ disable_merge_for_bucketing.q,\
+ empty_dir_in_table.q,\
+ exchgpartition2lel.q,\
+ external_table_with_space_in_location_path.q,\
+ file_with_header_footer.q,\
+ groupby2.q,\
+ import_exported_table.q,\
+ index_bitmap3.q,\
+ index_bitmap_auto.q,\
+ infer_bucket_sort_bucketed_table.q,\
+ infer_bucket_sort_dyn_part.q,\
+ infer_bucket_sort_map_operators.q,\
+ infer_bucket_sort_merge.q,\
+ infer_bucket_sort_num_buckets.q,\
+ infer_bucket_sort_reducers_power_two.q,\
+ input16_cc.q,\
+ insert_dir_distcp.q,\
+ join1.q,\
+ join_acid_non_acid.q,\
+ leftsemijoin_mr.q,\
+ list_bucket_dml_10.q,\
+ load_fs2.q,\
+ load_hdfs_file_with_space_in_the_name.q,\
+ non_native_window_udf.q, \
+ orc_merge_diff_fs.q,\
+ optrstat_groupby.q,\
+ parallel_orderby.q,\
+ quotedid_smb.q,\
+ reduce_deduplicate.q,\
+ remote_script.q,\
+ root_dir_external_table.q,\
+ schemeAuthority.q,\
+ schemeAuthority2.q,\
+ scriptfile1.q,\
+ scriptfile1_win.q,\
+ skewjoin_onesideskew.q,\
+ stats_counter.q,\
+ stats_counter_partitioned.q,\
+ table_nonprintable.q,\
+ temp_table_external.q,\
+ truncate_column_buckets.q,\
+ uber_reduce.q,\
+ udf_using.q
+
+# These tests are disabled for minimr
+# ql_rewrite_gbtoidx.q,\
+# ql_rewrite_gbtoidx_cbo_1.q,\
+# ql_rewrite_gbtoidx_cbo_2.q,\
+# smb_mapjoin_8.q,\
+
+
+# Tests that are not enabled for CLI Driver
+disabled.query.files=ql_rewrite_gbtoidx.q,\
+ ql_rewrite_gbtoidx_cbo_1.q,\
+ ql_rewrite_gbtoidx_cbo_2.q,\
+ rcfile_merge1.q,\
+ smb_mapjoin_8.q
+
+minitez.query.files.shared=acid_globallimit.q,\
+ empty_join.q,\
+ alter_merge_2_orc.q,\
+ alter_merge_orc.q,\
+ alter_merge_stats_orc.q,\
+ auto_join0.q,\
+ auto_join1.q,\
+ bucket2.q,\
+ bucket3.q,\
+ bucket4.q,\
+ cbo_gby.q,\
+ cbo_gby_empty.q,\
+ cbo_join.q,\
+ cbo_limit.q,\
+ cbo_semijoin.q,\
+ cbo_simple_select.q,\
+ cbo_stats.q,\
+ cbo_subq_exists.q,\
+ cbo_subq_in.q,\
+ cbo_subq_not_in.q,\
+ cbo_udf_udaf.q,\
+ cbo_union.q,\
+ cbo_views.q,\
+ cbo_windowing.q,\
+ correlationoptimizer1.q,\
+ count.q,\
+ create_merge_compressed.q,\
+ cross_join.q,\
+ cross_product_check_1.q,\
+ cross_product_check_2.q,\
+ ctas.q,\
+ custom_input_output_format.q,\
+ delete_all_non_partitioned.q,\
+ delete_all_partitioned.q,\
+ delete_orig_table.q,\
+ delete_tmp_table.q,\
+ delete_where_no_match.q,\
+ delete_where_non_partitioned.q,\
+ delete_where_partitioned.q,\
+ delete_whole_partition.q,\
+ disable_merge_for_bucketing.q,\
+ dynpart_sort_opt_vectorization.q,\
+ dynpart_sort_optimization.q,\
+ dynpart_sort_optimization2.q,\
+ enforce_order.q,\
+ filter_join_breaktask.q,\
+ filter_join_breaktask2.q,\
+ groupby1.q,\
+ groupby2.q,\
+ groupby3.q,\
+ having.q,\
+ identity_project_remove_skip.q\
+ insert1.q,\
+ insert_into1.q,\
+ insert_into2.q,\
+ insert_orig_table.q,\
+ insert_values_dynamic_partitioned.q,\
+ insert_values_non_partitioned.q,\
+ insert_values_orig_table.q\
+ insert_values_partitioned.q,\
+ insert_values_tmp_table.q,\
+ insert_update_delete.q,\
+ join0.q,\
+ join1.q,\
+ join_nullsafe.q,\
+ leftsemijoin.q,\
+ limit_pushdown.q,\
+ load_dyn_part1.q,\
+ load_dyn_part2.q,\
+ load_dyn_part3.q,\
+ mapjoin_mapjoin.q,\
+ mapreduce1.q,\
+ mapreduce2.q,\
+ merge1.q,\
+ merge2.q,\
+ mergejoin.q,\
+ metadataonly1.q,\
+ metadata_only_queries.q,\
+ metadata_only_queries_with_filters.q,\
+ nonmr_fetch_threshold.q,\
+ optimize_nullscan.q,\
+ orc_analyze.q,\
+ orc_merge1.q,\
+ orc_merge2.q,\
+ orc_merge3.q,\
+ orc_merge4.q,\
+ orc_merge5.q,\
+ orc_merge6.q,\
+ orc_merge7.q,\
+ orc_merge8.q,\
+ orc_merge9.q,\
+ orc_merge10.q,\
+ orc_merge11.q,\
+ orc_merge12.q,\
+ orc_merge_incompat1.q,\
+ orc_merge_incompat2.q,\
+ orc_merge_incompat3.q,\
+ orc_vectorization_ppd.q,\
+ parallel.q,\
+ ptf.q,\
+ ptf_matchpath.q,\
+ ptf_streaming.q,\
+ sample1.q,\
+ schema_evol_text_nonvec_mapwork_table.q,\
+ schema_evol_text_nonvec_fetchwork_table.q,\
+ schema_evol_orc_nonvec_fetchwork_part.q,\
+ schema_evol_orc_nonvec_mapwork_part.q,\
+ schema_evol_text_nonvec_fetchwork_part.q,\
+ schema_evol_text_nonvec_mapwork_part.q,\
+ schema_evol_orc_acid_mapwork_part.q,\
+ schema_evol_orc_acid_mapwork_table.q,\
+ schema_evol_orc_acidvec_mapwork_table.q,\
+ schema_evol_orc_acidvec_mapwork_part.q,\
+ schema_evol_orc_vec_mapwork_part.q,\
+ schema_evol_text_fetchwork_table.q,\
+ schema_evol_text_mapwork_table.q,\
+ schema_evol_orc_vec_mapwork_table.q,\
+ schema_evol_orc_nonvec_mapwork_table.q,\
+ schema_evol_orc_nonvec_fetchwork_table.q,\
+ selectDistinctStar.q,\
+ script_env_var1.q,\
+ script_env_var2.q,\
+ script_pipe.q,\
+ scriptfile1.q,\
+ select_dummy_source.q,\
+ skewjoin.q,\
+ stats_counter.q,\
+ stats_counter_partitioned.q,\
+ stats_noscan_1.q,\
+ stats_only_null.q,\
+ subquery_exists.q,\
+ subquery_in.q,\
+ temp_table.q,\
+ transform1.q,\
+ transform2.q,\
+ transform_ppr1.q,\
+ transform_ppr2.q,\
+ union2.q,\
+ union3.q,\
+ union4.q,\
+ union5.q,\
+ union6.q,\
+ union7.q,\
+ union8.q,\
+ union9.q,\
+ unionDistinct_1.q,\
+ unionDistinct_2.q,\
+ union_fast_stats.q,\
+ update_after_multiple_inserts.q,\
+ update_all_non_partitioned.q,\
+ update_all_partitioned.q,\
+ update_all_types.q,\
+ update_orig_table.q,\
+ update_tmp_table.q,\
+ update_where_no_match.q,\
+ update_where_non_partitioned.q,\
+ update_where_partitioned.q,\
+ update_two_cols.q,\
+ vector_acid3.q,\
+ vector_aggregate_9.q,\
+ vector_aggregate_without_gby.q,\
+ vector_auto_smb_mapjoin_14.q,\
+ vector_between_in.q,\
+ vector_between_columns.q,\
+ vector_binary_join_groupby.q,\
+ vector_bround.q,\
+ vector_bucket.q,\
+ vector_char_cast.q,\
+ vector_cast_constant.q,\
+ vector_char_2.q,\
+ vector_char_4.q,\
+ vector_char_mapjoin1.q,\
+ vector_char_simple.q,\
+ vector_coalesce.q,\
+ vector_coalesce_2.q,\
+ vector_complex_all.q,\
+ vector_count_distinct.q,\
+ vector_data_types.q,\
+ vector_date_1.q,\
+ vector_decimal_1.q,\
+ vector_decimal_10_0.q,\
+ vector_decimal_2.q,\
+ vector_decimal_3.q,\
+ vector_decimal_4.q,\
+ vector_decimal_5.q,\
+ vector_decimal_6.q,\
+ vector_decimal_aggregate.q,\
+ vector_decimal_cast.q,\
+ vector_decimal_expressions.q,\
+ vector_decimal_mapjoin.q,\
+ vector_decimal_math_funcs.q,\
+ vector_decimal_precision.q,\
+ vector_decimal_round.q,\
+ vector_decimal_round_2.q,\
+ vector_decimal_trailing.q,\
+ vector_decimal_udf.q,\
+ vector_decimal_udf2.q,\
+ vector_distinct_2.q,\
+ vector_elt.q,\
+ vector_groupby_3.q,\
+ vector_groupby_mapjoin.q,\
+ vector_groupby_reduce.q,\
+ vector_grouping_sets.q,\
+ vector_if_expr.q,\
+ vector_inner_join.q,\
+ vector_interval_1.q,\
+ vector_interval_2.q,\
+ vector_interval_mapjoin.q,\
+ vector_join30.q,\
+ vector_join_filters.q,\
+ vector_join_nulls.q,\
+ vector_left_outer_join.q,\
+ vector_left_outer_join2.q,\
+ vector_leftsemi_mapjoin.q,\
+ vector_mapjoin_reduce.q,\
+ vector_mr_diff_schema_alias.q,\
+ vector_multi_insert.q,\
+ vector_non_string_partition.q,\
+ vector_nullsafe_join.q,\
+ vector_null_projection.q,\
+ vector_nvl.q,\
+ vector_orderby_5.q,\
+ vector_outer_join0.q,\
+ vector_outer_join1.q,\
+ vector_outer_join2.q,\
+ vector_outer_join3.q,\
+ vector_outer_join4.q,\
+ vector_outer_join5.q,\
+ vector_outer_join6.q,\
+ vector_partition_diff_num_cols.q,\
+ vector_partitioned_date_time.q,\
+ vector_reduce_groupby_decimal.q,\
+ vector_reduce1.q,\
+ vector_reduce2.q,\
+ vector_reduce3.q,\
+ vector_string_concat.q,\
+ vector_struct_in.q,\
+ vector_varchar_4.q,\
+ vector_varchar_mapjoin1.q,\
+ vector_varchar_simple.q,\
+ vector_when_case_null.q,\
+ vectorization_0.q,\
+ vectorization_1.q,\
+ vectorization_10.q,\
+ vectorization_11.q,\
+ vectorization_12.q,\
+ vectorization_13.q,\
+ vectorization_14.q,\
+ vectorization_15.q,\
+ vectorization_16.q,\
+ vectorization_17.q,\
+ vectorization_2.q,\
+ vectorization_3.q,\
+ vectorization_4.q,\
+ vectorization_5.q,\
+ vectorization_6.q,\
+ vectorization_7.q,\
+ vectorization_8.q,\
+ vectorization_9.q,\
+ vectorization_decimal_date.q,\
+ vectorization_div0.q,\
+ vectorization_limit.q,\
+ vectorization_nested_udf.q,\
+ vectorization_not.q,\
+ vectorization_part.q,\
+ vectorization_part_project.q,\
+ vectorization_part_varchar.q,\
+ vectorization_pushdown.q,\
+ vectorization_short_regress.q,\
+ vectorized_bucketmapjoin1.q,\
+ vectorized_case.q,\
+ vectorized_casts.q,\
+ vectorized_context.q,\
+ vectorized_date_funcs.q,\
+ vectorized_distinct_gby.q,\
+ vectorized_mapjoin.q,\
+ vectorized_math_funcs.q,\
+ vectorized_nested_mapjoin.q,\
+ vectorized_parquet.q,\
+ vectorized_parquet_types.q,\
+ vectorized_ptf.q,\
+ vectorized_rcfile_columnar.q,\
+ vectorized_shufflejoin.q,\
+ vectorized_string_funcs.q,\
+ vectorized_timestamp_funcs.q,\
+ vectorized_timestamp_ints_casts.q,\
+ auto_sortmerge_join_1.q,\
+ auto_sortmerge_join_10.q,\
+ auto_sortmerge_join_11.q,\
+ auto_sortmerge_join_12.q,\
+ auto_sortmerge_join_13.q,\
+ auto_sortmerge_join_14.q,\
+ auto_sortmerge_join_15.q,\
+ auto_sortmerge_join_16.q,\
+ auto_sortmerge_join_2.q,\
+ auto_sortmerge_join_3.q,\
+ auto_sortmerge_join_4.q,\
+ auto_sortmerge_join_5.q,\
+ auto_sortmerge_join_6.q,\
+ auto_sortmerge_join_7.q,\
+ auto_sortmerge_join_8.q,\
+ auto_sortmerge_join_9.q,\
+ auto_join30.q,\
+ auto_join21.q,\
+ auto_join29.q,\
+ auto_join_filters.q,\
+ auto_join_nulls.q,\
+ union_type_chk.q
+
+
+minitez.query.files=bucket_map_join_tez1.q,\
+ smb_cache.q,\
+ bucket_map_join_tez2.q,\
+ constprog_dpp.q,\
+ dynamic_partition_pruning.q,\
+ dynamic_partition_pruning_2.q,\
+ bucketpruning1.q,\
+ explainuser_1.q,\
+ explainuser_2.q,\
+ explainuser_3.q,\
+ hybridgrace_hashjoin_1.q,\
+ hybridgrace_hashjoin_2.q,\
+ mapjoin_decimal.q,\
+ mergejoin_3way.q,\
+ lvj_mapjoin.q,\
+ llapdecider.q,\
+ mrr.q,\
+ orc_ppd_basic.q,\
+ orc_merge_diff_fs.q,\
+ tez_bmj_schema_evolution.q,\
+ tez_dml.q,\
+ tez_fsstat.q,\
+ tez_insert_overwrite_local_directory_1.q,\
+ tez_dynpart_hashjoin_1.q,\
+ tez_dynpart_hashjoin_2.q,\
+ tez_dynpart_hashjoin_3.q,\
+ tez_vector_dynpart_hashjoin_1.q,\
+ tez_vector_dynpart_hashjoin_2.q,\
+ tez_join_hash.q,\
+ tez_join_result_complex.q,\
+ tez_join_tests.q,\
+ tez_joins_explain.q,\
+ tez_schema_evolution.q,\
+ tez_self_join.q,\
+ tez_union.q,\
+ tez_union2.q,\
+ tez_union_dynamic_partition.q,\
+ tez_union_view.q,\
+ tez_union_with_udf.q,\
+ tez_union_decimal.q,\
+ tez_union_group_by.q,\
+ tez_smb_main.q,\
+ tez_smb_1.q,\
+ tez_smb_empty.q,\
+ vector_join_part_col_char.q,\
+ vectorized_dynamic_partition_pruning.q,\
+ tez_multi_union.q,\
+ tez_join.q,\
+ tez_union_multiinsert.q,\
+ windowing_gby.q
+
+
+
+
+minillap.query.files=bucket_map_join_tez1.q,\
+ bucket_map_join_tez2.q,\
+ constprog_dpp.q,\
+ dynamic_partition_pruning.q,\
+ dynamic_partition_pruning_2.q,\
+ hybridgrace_hashjoin_1.q,\
+ hybridgrace_hashjoin_2.q,\
+ mapjoin_decimal.q,\
+ lvj_mapjoin.q,\
+ llapdecider.q,\
+ mrr.q,\
+ orc_ppd_basic.q,\
+ tez_bmj_schema_evolution.q,\
+ tez_dml.q,\
+ tez_fsstat.q,\
+ tez_insert_overwrite_local_directory_1.q,\
+ tez_dynpart_hashjoin_1.q,\
+ tez_dynpart_hashjoin_2.q,\
+ tez_vector_dynpart_hashjoin_1.q,\
+ tez_vector_dynpart_hashjoin_2.q,\
+ tez_join_hash.q,\
+ tez_join_result_complex.q,\
+ tez_join_tests.q,\
+ tez_joins_explain.q,\
+ tez_schema_evolution.q,\
+ tez_self_join.q,\
+ tez_union.q,\
+ tez_union2.q,\
+ tez_union_dynamic_partition.q,\
+ tez_union_view.q,\
+ tez_union_decimal.q,\
+ tez_union_group_by.q,\
+ tez_smb_main.q,\
+ tez_smb_1.q,\
+ vector_join_part_col_char.q,\
+ vectorized_dynamic_partition_pruning.q,\
+ tez_multi_union.q,\
+ tez_join.q,\
+ tez_union_multiinsert.q
+
+encrypted.query.files=encryption_join_unencrypted_tbl.q,\
+ encryption_insert_partition_static.q,\
+ encryption_insert_partition_dynamic.q,\
+ encryption_join_with_different_encryption_keys.q,\
+ encryption_select_read_only_encrypted_tbl.q,\
+ encryption_select_read_only_unencrypted_tbl.q,\
+ encryption_load_data_to_encrypted_tables.q, \
+ encryption_unencrypted_nonhdfs_external_tables.q \
+ encryption_move_tbl.q \
+ encryption_drop_table.q \
+ encryption_insert_values.q \
+ encryption_drop_view.q \
+ encryption_drop_partition.q \
+ encryption_with_trash.q
+
+beeline.positive.exclude=add_part_exist.q,\
+ alter1.q,\
+ alter2.q,\
+ alter4.q,\
+ alter5.q,\
+ alter_rename_partition.q,\
+ alter_rename_partition_authorization.q,\
+ archive.q,\
+ archive_corrupt.q,\
+ archive_mr_1806.q,\
+ archive_multi.q,\
+ archive_multi_mr_1806.q,\
+ authorization_1.q,\
+ authorization_2.q,\
+ authorization_4.q,\
+ authorization_5.q,\
+ authorization_6.q,\
+ authorization_7.q,\
+ ba_table1.q,\
+ ba_table2.q,\
+ ba_table3.q,\
+ ba_table_udfs.q,\
+ binary_table_bincolserde.q,\
+ binary_table_colserde.q,\
+ cluster.q,\
+ columnarserde_create_shortcut.q,\
+ combine2.q,\
+ constant_prop.q,\
+ create_nested_type.q,\
+ create_or_replace_view.q,\
+ create_struct_table.q,\
+ create_union_table.q,\
+ database.q,\
+ database_location.q,\
+ database_properties.q,\
+ describe_database_json.q,\
+ drop_database_removes_partition_dirs.q,\
+ escape1.q,\
+ escape2.q,\
+ exim_00_nonpart_empty.q,\
+ exim_01_nonpart.q,\
+ exim_02_00_part_empty.q,\
+ exim_02_part.q,\
+ exim_03_nonpart_over_compat.q,\
+ exim_04_all_part.q,\
+ exim_04_evolved_parts.q,\
+ exim_05_some_part.q,\
+ exim_06_one_part.q,\
+ exim_07_all_part_over_nonoverlap.q,\
+ exim_08_nonpart_rename.q,\
+ exim_09_part_spec_nonoverlap.q,\
+ exim_10_external_managed.q,\
+ exim_11_managed_external.q,\
+ exim_12_external_location.q,\
+ exim_13_managed_location.q,\
+ exim_14_managed_location_over_existing.q,\
+ exim_15_external_part.q,\
+ exim_16_part_external.q,\
+ exim_17_part_managed.q,\
+ exim_18_part_external.q,\
+ exim_19_00_part_external_location.q,\
+ exim_19_part_external_location.q,\
+ exim_20_part_managed_location.q,\
+ exim_21_export_authsuccess.q,\
+ exim_22_import_exist_authsuccess.q,\
+ exim_23_import_part_authsuccess.q,\
+ exim_24_import_nonexist_authsuccess.q,\
+ global_limit.q,\
+ groupby_complex_types.q,\
+ groupby_complex_types_multi_single_reducer.q,\
+ index_auth.q,\
+ index_auto.q,\
+ index_auto_empty.q,\
+ index_bitmap.q,\
+ index_bitmap1.q,\
+ index_bitmap2.q,\
+ index_bitmap3.q,\
+ index_bitmap_auto.q,\
+ index_bitmap_rc.q,\
+ index_compact.q,\
+ index_compact_1.q,\
+ index_compact_2.q,\
+ index_compact_3.q,\
+ index_stale_partitioned.q,\
+ init_file.q,\
+ input16.q,\
+ input16_cc.q,\
+ input46.q,\
+ input_columnarserde.q,\
+ input_dynamicserde.q,\
+ input_lazyserde.q,\
+ input_testxpath3.q,\
+ input_testxpath4.q,\
+ insert2_overwrite_partitions.q,\
+ insertexternal1.q,\
+ join_thrift.q,\
+ lateral_view.q,\
+ load_binary_data.q,\
+ load_exist_part_authsuccess.q,\
+ load_nonpart_authsuccess.q,\
+ load_part_authsuccess.q,\
+ loadpart_err.q,\
+ lock1.q,\
+ lock2.q,\
+ lock3.q,\
+ lock4.q,\
+ merge_dynamic_partition.q,\
+ multi_insert.q,\
+ multi_insert_move_tasks_share_dependencies.q,\
+ null_column.q,\
+ ppd_clusterby.q,\
+ query_with_semi.q,\
+ rename_column.q,\
+ sample6.q,\
+ sample_islocalmode_hook.q,\
+ set_processor_namespaces.q,\
+ show_tables.q,\
+ source.q,\
+ split_sample.q,\
+ str_to_map.q,\
+ transform1.q,\
+ udaf_collect_set.q,\
+ udaf_context_ngrams.q,\
+ udaf_histogram_numeric.q,\
+ udaf_ngrams.q,\
+ udaf_percentile_approx.q,\
+ udf_array.q,\
+ udf_bitmap_and.q,\
+ udf_bitmap_or.q,\
+ udf_explode.q,\
+ udf_format_number.q,\
+ udf_map.q,\
+ udf_map_keys.q,\
+ udf_map_values.q,\
+ udf_max.q,\
+ udf_min.q,\
+ udf_named_struct.q,\
+ udf_percentile.q,\
+ udf_printf.q,\
+ udf_sentences.q,\
+ udf_sort_array.q,\
+ udf_split.q,\
+ udf_struct.q,\
+ udf_substr.q,\
+ udf_translate.q,\
+ udf_union.q,\
+ udf_xpath.q,\
+ udtf_stack.q,\
+ view.q,\
+ virtual_column.q
+
+minimr.query.negative.files=cluster_tasklog_retrieval.q,\
+ file_with_header_footer_negative.q,\
+ local_mapred_error_cache.q,\
+ mapreduce_stack_trace.q,\
+ mapreduce_stack_trace_hadoop20.q,\
+ mapreduce_stack_trace_turnoff.q,\
+ mapreduce_stack_trace_turnoff_hadoop20.q,\
+ minimr_broken_pipe.q,\
+ table_nonprintable_negative.q,\
+ udf_local_resource.q
+
+# tests are sorted use: perl -pe 's@\\\s*\n@ @g' testconfiguration.properties \
+# | awk -F= '/spark.query.files/{print $2}' | perl -pe 's@.q *, *@\n@g' \
+# | egrep -v '^ *$' | sort -V | uniq | perl -pe 's@\n@.q, \\\n@g' | perl -pe 's@^@ @g'
+spark.query.files=add_part_multiple.q, \
+ alter_merge_orc.q, \
+ alter_merge_stats_orc.q, \
+ annotate_stats_join.q, \
+ auto_join0.q, \
+ auto_join1.q, \
+ auto_join10.q, \
+ auto_join11.q, \
+ auto_join12.q, \
+ auto_join13.q, \
+ auto_join14.q, \
+ auto_join15.q, \
+ auto_join16.q, \
+ auto_join17.q, \
+ auto_join18.q, \
+ auto_join18_multi_distinct.q, \
+ auto_join19.q, \
+ auto_join2.q, \
+ auto_join20.q, \
+ auto_join21.q, \
+ auto_join22.q, \
+ auto_join23.q, \
+ auto_join24.q, \
+ auto_join26.q, \
+ auto_join27.q, \
+ auto_join28.q, \
+ auto_join29.q, \
+ auto_join3.q, \
+ auto_join30.q, \
+ auto_join31.q, \
+ auto_join32.q, \
+ auto_join4.q, \
+ auto_join5.q, \
+ auto_join6.q, \
+ auto_join7.q, \
+ auto_join8.q, \
+ auto_join9.q, \
+ auto_join_filters.q, \
+ auto_join_nulls.q, \
+ auto_join_reordering_values.q, \
+ auto_join_stats.q, \
+ auto_join_stats2.q, \
+ auto_join_without_localtask.q, \
+ auto_smb_mapjoin_14.q, \
+ auto_sortmerge_join_1.q, \
+ auto_sortmerge_join_10.q, \
+ auto_sortmerge_join_12.q, \
+ auto_sortmerge_join_13.q, \
+ auto_sortmerge_join_14.q, \
+ auto_sortmerge_join_15.q, \
+ auto_sortmerge_join_16.q, \
+ auto_sortmerge_join_2.q, \
+ auto_sortmerge_join_3.q, \
+ auto_sortmerge_join_4.q, \
+ auto_sortmerge_join_5.q, \
+ auto_sortmerge_join_6.q, \
+ auto_sortmerge_join_7.q, \
+ auto_sortmerge_join_8.q, \
+ auto_sortmerge_join_9.q, \
+ avro_compression_enabled_native.q, \
+ avro_decimal_native.q, \
+ avro_joins.q, \
+ avro_joins_native.q, \
+ bucket2.q, \
+ bucket3.q, \
+ bucket4.q, \
+ bucket_map_join_1.q, \
+ bucket_map_join_2.q, \
+ bucket_map_join_spark1.q, \
+ bucket_map_join_spark2.q, \
+ bucket_map_join_spark3.q, \
+ bucket_map_join_spark4.q, \
+ bucket_map_join_tez1.q, \
+ bucket_map_join_tez2.q, \
+ bucketmapjoin1.q, \
+ bucketmapjoin10.q, \
+ bucketmapjoin11.q, \
+ bucketmapjoin12.q, \
+ bucketmapjoin13.q, \
+ bucketmapjoin2.q, \
+ bucketmapjoin3.q, \
+ bucketmapjoin4.q, \
+ bucketmapjoin5.q, \
+ bucketmapjoin7.q, \
+ bucketmapjoin8.q, \
+ bucketmapjoin9.q, \
+ bucketmapjoin_negative.q, \
+ bucketmapjoin_negative2.q, \
+ bucketmapjoin_negative3.q, \
+ bucketsortoptimize_insert_2.q, \
+ bucketsortoptimize_insert_4.q, \
+ bucketsortoptimize_insert_6.q, \
+ bucketsortoptimize_insert_7.q, \
+ bucketsortoptimize_insert_8.q, \
+ cbo_gby.q, \
+ cbo_gby_empty.q, \
+ cbo_limit.q, \
+ cbo_semijoin.q, \
+ cbo_simple_select.q, \
+ cbo_stats.q, \
+ cbo_subq_in.q, \
+ cbo_subq_not_in.q, \
+ cbo_udf_udaf.q, \
+ cbo_union.q, \
+ column_access_stats.q, \
+ count.q, \
+ create_merge_compressed.q, \
+ cross_join.q, \
+ cross_product_check_1.q, \
+ cross_product_check_2.q, \
+ ctas.q, \
+ custom_input_output_format.q, \
+ date_join1.q, \
+ date_udf.q, \
+ decimal_1_1.q, \
+ decimal_join.q, \
+ disable_merge_for_bucketing.q, \
+ dynamic_rdd_cache.q, \
+ enforce_order.q, \
+ escape_clusterby1.q, \
+ escape_distributeby1.q, \
+ escape_orderby1.q, \
+ escape_sortby1.q, \
+ filter_join_breaktask.q, \
+ filter_join_breaktask2.q, \
+ groupby1.q, \
+ groupby10.q, \
+ groupby11.q, \
+ groupby1_map.q, \
+ groupby1_map_nomap.q, \
+ groupby1_map_skew.q, \
+ groupby1_noskew.q, \
+ groupby2.q, \
+ groupby2_map.q, \
+ groupby2_map_multi_distinct.q, \
+ groupby2_map_skew.q, \
+ groupby2_noskew.q, \
+ groupby2_noskew_multi_distinct.q, \
+ groupby3.q, \
+ groupby3_map.q, \
+ groupby3_map_multi_distinct.q, \
+ groupby3_map_skew.q, \
+ groupby3_noskew.q, \
+ groupby3_noskew_multi_distinct.q, \
+ groupby4.q, \
+ groupby4_map.q, \
+ groupby4_map_skew.q, \
+ groupby4_noskew.q, \
+ groupby5.q, \
+ groupby5_map.q, \
+ groupby5_map_skew.q, \
+ groupby5_noskew.q, \
+ groupby6.q, \
+ groupby6_map.q, \
+ groupby6_map_skew.q, \
+ groupby6_noskew.q, \
+ groupby7.q, \
+ groupby7_map.q, \
+ groupby7_map_multi_single_reducer.q, \
+ groupby7_map_skew.q, \
+ groupby7_noskew.q, \
+ groupby7_noskew_multi_single_reducer.q, \
+ groupby8.q, \
+ groupby8_map.q, \
+ groupby8_map_skew.q, \
+ groupby8_noskew.q, \
+ groupby9.q, \
+ groupby_bigdata.q, \
+ groupby_complex_types.q, \
+ groupby_complex_types_multi_single_reducer.q, \
+ groupby_cube1.q, \
+ groupby_grouping_id2.q, \
+ groupby_map_ppr.q, \
+ groupby_map_ppr_multi_distinct.q, \
+ groupby_multi_insert_common_distinct.q, \
+ groupby_multi_single_reducer.q, \
+ groupby_multi_single_reducer2.q, \
+ groupby_multi_single_reducer3.q, \
+ groupby_position.q, \
+ groupby_ppr.q, \
+ groupby_ppr_multi_distinct.q, \
+ groupby_resolution.q, \
+ groupby_rollup1.q, \
+ groupby_sort_1_23.q, \
+ groupby_sort_skew_1.q, \
+ groupby_sort_skew_1_23.q, \
+ having.q, \
+ identity_project_remove_skip.q, \
+ index_auto_self_join.q, \
+ innerjoin.q, \
+ input12.q, \
+ input13.q, \
+ input14.q, \
+ input17.q, \
+ input18.q, \
+ input1_limit.q, \
+ input_part2.q, \
+ insert1.q, \
+ insert_into1.q, \
+ insert_into2.q, \
+ insert_into3.q, \
+ join0.q, \
+ join1.q, \
+ join10.q, \
+ join11.q, \
+ join12.q, \
+ join13.q, \
+ join14.q, \
+ join15.q, \
+ join16.q, \
+ join17.q, \
+ join18.q, \
+ join18_multi_distinct.q, \
+ join19.q, \
+ join2.q, \
+ join20.q, \
+ join21.q, \
+ join22.q, \
+ join23.q, \
+ join24.q, \
+ join25.q, \
+ join26.q, \
+ join27.q, \
+ join28.q, \
+ join29.q, \
+ join3.q, \
+ join30.q, \
+ join31.q, \
+ join32.q, \
+ join32_lessSize.q, \
+ join33.q, \
+ join34.q, \
+ join35.q, \
+ join36.q, \
+ join37.q, \
+ join38.q, \
+ join39.q, \
+ join4.q, \
+ join40.q, \
+ join41.q, \
+ join5.q, \
+ join6.q, \
+ join7.q, \
+ join8.q, \
+ join9.q, \
+ join_1to1.q, \
+ join_alt_syntax.q, \
+ join_array.q, \
+ join_casesensitive.q, \
+ join_cond_pushdown_1.q, \
+ join_cond_pushdown_2.q, \
+ join_cond_pushdown_3.q, \
+ join_cond_pushdown_4.q, \
+ join_cond_pushdown_unqual1.q, \
+ join_cond_pushdown_unqual2.q, \
+ join_cond_pushdown_unqual3.q, \
+ join_cond_pushdown_unqual4.q, \
+ join_empty.q, \
+ join_filters_overlap.q, \
+ join_hive_626.q, \
+ join_literals.q, \
+ join_map_ppr.q, \
+ join_merge_multi_expressions.q, \
+ join_merging.q, \
+ join_nullsafe.q, \
+ join_rc.q, \
+ join_reorder.q, \
+ join_reorder2.q, \
+ join_reorder3.q, \
+ join_reorder4.q, \
+ join_star.q, \
+ join_thrift.q, \
+ join_vc.q, \
+ join_view.q, \
+ lateral_view_explode2.q, \
+ leftsemijoin.q, \
+ leftsemijoin_mr.q, \
+ limit_partition_metadataonly.q, \
+ limit_pushdown.q, \
+ list_bucket_dml_2.q, \
+ load_dyn_part1.q, \
+ load_dyn_part10.q, \
+ load_dyn_part11.q, \
+ load_dyn_part12.q, \
+ load_dyn_part13.q, \
+ load_dyn_part14.q, \
+ load_dyn_part15.q, \
+ load_dyn_part2.q, \
+ load_dyn_part3.q, \
+ load_dyn_part4.q, \
+ load_dyn_part5.q, \
+ load_dyn_part6.q, \
+ load_dyn_part7.q, \
+ load_dyn_part8.q, \
+ load_dyn_part9.q, \
+ louter_join_ppr.q, \
+ mapjoin1.q, \
+ mapjoin_addjar.q, \
+ mapjoin_decimal.q, \
+ mapjoin_distinct.q, \
+ mapjoin_filter_on_outerjoin.q, \
+ mapjoin_mapjoin.q, \
+ mapjoin_memcheck.q, \
+ mapjoin_subquery.q, \
+ mapjoin_subquery2.q, \
+ mapjoin_test_outer.q, \
+ mapreduce1.q, \
+ mapreduce2.q, \
+ merge1.q, \
+ merge2.q, \
+ mergejoins.q, \
+ mergejoins_mixed.q, \
+ metadata_only_queries.q, \
+ metadata_only_queries_with_filters.q, \
+ multi_insert.q, \
+ multi_insert_gby.q, \
+ multi_insert_gby2.q, \
+ multi_insert_gby3.q, \
+ multi_insert_lateral_view.q, \
+ multi_insert_mixed.q, \
+ multi_insert_move_tasks_share_dependencies.q, \
+ multi_join_union.q, \
+ multi_join_union_src.q, \
+ multigroupby_singlemr.q, \
+ nullgroup.q, \
+ nullgroup2.q, \
+ nullgroup4.q, \
+ nullgroup4_multi_distinct.q, \
+ optimize_nullscan.q, \
+ order.q, \
+ order2.q, \
+ outer_join_ppr.q, \
+ parallel.q, \
+ parallel_join0.q, \
+ parallel_join1.q, \
+ parquet_join.q, \
+ pcr.q, \
+ ppd_gby_join.q, \
+ ppd_join.q, \
+ ppd_join2.q, \
+ ppd_join3.q, \
+ ppd_join4.q, \
+ ppd_join5.q, \
+ ppd_join_filter.q, \
+ ppd_multi_insert.q, \
+ ppd_outer_join1.q, \
+ ppd_outer_join2.q, \
+ ppd_outer_join3.q, \
+ ppd_outer_join4.q, \
+ ppd_outer_join5.q, \
+ ppd_transform.q, \
+ ptf.q, \
+ ptf_decimal.q, \
+ ptf_general_queries.q, \
+ ptf_matchpath.q, \
+ ptf_rcfile.q, \
+ ptf_register_tblfn.q, \
+ ptf_seqfile.q, \
+ ptf_streaming.q, \
+ rcfile_bigdata.q, \
+ reduce_deduplicate_exclude_join.q, \
+ router_join_ppr.q, \
+ runtime_skewjoin_mapjoin_spark.q, \
+ sample1.q, \
+ sample10.q, \
+ sample2.q, \
+ sample3.q, \
+ sample4.q, \
+ sample5.q, \
+ sample6.q, \
+ sample7.q, \
+ sample8.q, \
+ sample9.q, \
+ script_env_var1.q, \
+ script_env_var2.q, \
+ script_pipe.q, \
+ scriptfile1.q, \
+ semijoin.q, \
+ skewjoin.q, \
+ skewjoin_noskew.q, \
+ skewjoin_union_remove_1.q, \
+ skewjoin_union_remove_2.q, \
+ skewjoinopt1.q, \
+ skewjoinopt10.q, \
+ skewjoinopt11.q, \
+ skewjoinopt12.q, \
+ skewjoinopt13.q, \
+ skewjoinopt14.q, \
+ skewjoinopt15.q, \
+ skewjoinopt16.q, \
+ skewjoinopt17.q, \
+ skewjoinopt18.q, \
+ skewjoinopt19.q, \
+ skewjoinopt2.q, \
+ skewjoinopt20.q, \
+ skewjoinopt3.q, \
+ skewjoinopt4.q, \
+ skewjoinopt5.q, \
+ skewjoinopt6.q, \
+ skewjoinopt7.q, \
+ skewjoinopt8.q, \
+ skewjoinopt9.q, \
+ smb_mapjoin_1.q, \
+ smb_mapjoin_10.q, \
+ smb_mapjoin_11.q, \
+ smb_mapjoin_12.q, \
+ smb_mapjoin_13.q, \
+ smb_mapjoin_14.q, \
+ smb_mapjoin_15.q, \
+ smb_mapjoin_16.q, \
+ smb_mapjoin_17.q, \
+ smb_mapjoin_18.q, \
+ smb_mapjoin_19.q, \
+ smb_mapjoin_2.q, \
+ smb_mapjoin_20.q, \
+ smb_mapjoin_21.q, \
+ smb_mapjoin_22.q, \
+ smb_mapjoin_25.q, \
+ smb_mapjoin_3.q, \
+ smb_mapjoin_4.q, \
+ smb_mapjoin_5.q, \
+ smb_mapjoin_6.q, \
+ smb_mapjoin_7.q, \
+ smb_mapjoin_8.q, \
+ smb_mapjoin_9.q, \
+ sort.q, \
+ stats0.q, \
+ stats1.q, \
+ stats10.q, \
+ stats12.q, \
+ stats13.q, \
+ stats14.q, \
+ stats15.q, \
+ stats16.q, \
+ stats18.q, \
+ stats2.q, \
+ stats20.q, \
+ stats3.q, \
+ stats5.q, \
+ stats6.q, \
+ stats7.q, \
+ stats8.q, \
+ stats9.q, \
+ stats_counter.q, \
+ stats_counter_partitioned.q, \
+ stats_noscan_1.q, \
+ stats_noscan_2.q, \
+ stats_only_null.q, \
+ stats_partscan_1_23.q, \
+ statsfs.q, \
+ subquery_exists.q, \
+ subquery_in.q, \
+ subquery_multiinsert.q, \
+ table_access_keys_stats.q, \
+ temp_table.q, \
+ temp_table_gb1.q, \
+ temp_table_join1.q, \
+ tez_join_tests.q, \
+ tez_joins_explain.q, \
+ timestamp_1.q, \
+ timestamp_2.q, \
+ timestamp_3.q, \
+ timestamp_comparison.q, \
+ timestamp_lazy.q, \
+ timestamp_null.q, \
+ timestamp_udf.q, \
+ transform1.q, \
+ transform2.q, \
+ transform_ppr1.q, \
+ transform_ppr2.q, \
+ udaf_collect_set.q, \
+ udf_example_add.q, \
+ udf_in_file.q, \
+ udf_max.q, \
+ udf_min.q, \
+ udf_percentile.q, \
+ union.q, \
+ union10.q, \
+ union11.q, \
+ union12.q, \
+ union13.q, \
+ union14.q, \
+ union15.q, \
+ union16.q, \
+ union17.q, \
+ union18.q, \
+ union19.q, \
+ union2.q, \
+ union20.q, \
+ union21.q, \
+ union22.q, \
+ union23.q, \
+ union24.q, \
+ union25.q, \
+ union26.q, \
+ union27.q, \
+ union28.q, \
+ union29.q, \
+ union3.q, \
+ union30.q, \
+ union31.q, \
+ union32.q, \
+ union33.q, \
+ union34.q, \
+ union4.q, \
+ union5.q, \
+ union6.q, \
+ union7.q, \
+ union8.q, \
+ union9.q, \
+ union_date.q, \
+ union_date_trim.q, \
+ union_lateralview.q, \
+ union_null.q, \
+ union_ppr.q, \
+ union_remove_1.q, \
+ union_remove_10.q, \
+ union_remove_11.q, \
+ union_remove_12.q, \
+ union_remove_13.q, \
+ union_remove_14.q, \
+ union_remove_15.q, \
+ union_remove_16.q, \
+ union_remove_17.q, \
+ union_remove_18.q, \
+ union_remove_19.q, \
+ union_remove_2.q, \
+ union_remove_20.q, \
+ union_remove_21.q, \
+ union_remove_22.q, \
+ union_remove_23.q, \
+ union_remove_24.q, \
+ union_remove_25.q, \
+ union_remove_3.q, \
+ union_remove_4.q, \
+ union_remove_5.q, \
+ union_remove_6.q, \
+ union_remove_6_subq.q, \
+ union_remove_7.q, \
+ union_remove_8.q, \
+ union_remove_9.q, \
+ union_script.q, \
+ union_top_level.q, \
+ union_view.q, \
+ uniquejoin.q, \
+ varchar_join1.q, \
+ vector_between_in.q, \
+ vector_cast_constant.q, \
+ vector_char_4.q, \
+ vector_count_distinct.q, \
+ vector_data_types.q, \
+ vector_decimal_aggregate.q, \
+ vector_decimal_mapjoin.q, \
+ vector_distinct_2.q, \
+ vector_elt.q, \
+ vector_groupby_3.q, \
+ vector_left_outer_join.q, \
+ vector_mapjoin_reduce.q, \
+ vector_orderby_5.q, \
+ vector_string_concat.q, \
+ vector_varchar_4.q, \
+ vectorization_0.q, \
+ vectorization_1.q, \
+ vectorization_10.q, \
+ vectorization_11.q, \
+ vectorization_12.q, \
+ vectorization_13.q, \
+ vectorization_14.q, \
+ vectorization_15.q, \
+ vectorization_16.q, \
+ vectorization_17.q, \
+ vectorization_2.q, \
+ vectorization_3.q, \
+ vectorization_4.q, \
+ vectorization_5.q, \
+ vectorization_6.q, \
+ vectorization_9.q, \
+ vectorization_decimal_date.q, \
+ vectorization_div0.q, \
+ vectorization_nested_udf.q, \
+ vectorization_not.q, \
+ vectorization_part.q, \
+ vectorization_part_project.q, \
+ vectorization_pushdown.q, \
+ vectorization_short_regress.q, \
+ vectorized_case.q, \
+ vectorized_mapjoin.q, \
+ vectorized_math_funcs.q, \
+ vectorized_nested_mapjoin.q, \
+ vectorized_ptf.q, \
+ vectorized_rcfile_columnar.q, \
+ vectorized_shufflejoin.q, \
+ vectorized_string_funcs.q, \
+ vectorized_timestamp_funcs.q, \
+ windowing.q
+
+# Unlike "spark.query.files" above, these tests only run
+# under Spark engine.
+spark.only.query.files=spark_dynamic_partition_pruning.q,\
+ spark_dynamic_partition_pruning_2.q,\
+ spark_vectorized_dynamic_partition_pruning.q
+
+miniSparkOnYarn.query.files=auto_sortmerge_join_16.q,\
+ bucket4.q,\
+ bucket5.q,\
+ bucket6.q,\
+ bucketizedhiveinputformat.q,\
+ bucketmapjoin6.q,\
+ bucketmapjoin7.q,\
+ constprog_partitioner.q,\
+ disable_merge_for_bucketing.q,\
+ empty_dir_in_table.q,\
+ external_table_with_space_in_location_path.q,\
+ file_with_header_footer.q,\
+ import_exported_table.q,\
+ index_bitmap3.q,\
+ index_bitmap_auto.q,\
+ infer_bucket_sort_bucketed_table.q,\
+ infer_bucket_sort_map_operators.q,\
+ infer_bucket_sort_merge.q,\
+ infer_bucket_sort_num_buckets.q,\
+ infer_bucket_sort_reducers_power_two.q,\
+ input16_cc.q,\
+ leftsemijoin_mr.q,\
+ list_bucket_dml_10.q,\
+ load_fs2.q,\
+ load_hdfs_file_with_space_in_the_name.q,\
+ optrstat_groupby.q,\
+ orc_merge1.q,\
+ orc_merge2.q,\
+ orc_merge3.q,\
+ orc_merge4.q,\
+ orc_merge5.q,\
+ orc_merge6.q,\
+ orc_merge7.q,\
+ orc_merge8.q,\
+ orc_merge9.q,\
+ orc_merge_diff_fs.q,\
+ orc_merge_incompat1.q,\
+ orc_merge_incompat2.q,\
+ parallel_orderby.q,\
+ quotedid_smb.q,\
+ reduce_deduplicate.q,\
+ remote_script.q,\
+ root_dir_external_table.q,\
+ schemeAuthority.q,\
+ schemeAuthority2.q,\
+ scriptfile1.q,\
+ scriptfile1_win.q,\
+ stats_counter.q,\
+ stats_counter_partitioned.q,\
+ temp_table_external.q,\
+ truncate_column_buckets.q,\
+ uber_reduce.q,\
+ vector_inner_join.q,\
+ vector_outer_join0.q,\
+ vector_outer_join1.q,\
+ vector_outer_join2.q,\
+ vector_outer_join3.q,\
+ vector_outer_join4.q,\
+ vector_outer_join5.q
+
+# These tests are removed from miniSparkOnYarn.query.files
+# ql_rewrite_gbtoidx.q,\
+# ql_rewrite_gbtoidx_cbo_1.q,\
+# smb_mapjoin_8.q,\
+
+
+spark.query.negative.files=groupby2_map_skew_multi_distinct.q,\
+ groupby2_multi_distinct.q,\
+ groupby3_map_skew_multi_distinct.q,\
+ groupby3_multi_distinct.q,\
+ groupby_grouping_sets7.q
http://git-wip-us.apache.org/repos/asf/hive/blob/ca11c393/orc/src/java/org/apache/orc/impl/WriterImpl.java
----------------------------------------------------------------------
diff --git a/orc/src/java/org/apache/orc/impl/WriterImpl.java b/orc/src/java/org/apache/orc/impl/WriterImpl.java
index 7eaa761..f8afe06 100644
--- a/orc/src/java/org/apache/orc/impl/WriterImpl.java
+++ b/orc/src/java/org/apache/orc/impl/WriterImpl.java
@@ -1735,19 +1735,17 @@ public class WriterImpl implements Writer, MemoryManager.Callback {
int length) throws IOException {
super.writeBatch(vector, offset, length);
TimestampColumnVector vec = (TimestampColumnVector) vector;
+ Timestamp val;
if (vector.isRepeating) {
if (vector.noNulls || !vector.isNull[0]) {
- long millis = vec.getEpochMilliseconds(0);
- int adjustedNanos = vec.getSignedNanos(0);
- if (adjustedNanos < 0) {
- adjustedNanos += NANOS_PER_SECOND;
- }
+ val = vec.asScratchTimestamp(0);
+ long millis = val.getTime();
indexStatistics.updateTimestamp(millis);
if (createBloomFilter) {
bloomFilter.addLong(millis);
}
- final long secs = vec.getEpochSeconds(0) - base_timestamp;
- final long nano = formatNanos(adjustedNanos);
+ final long secs = millis / MILLIS_PER_SECOND - base_timestamp;
+ final long nano = formatNanos(val.getNanos());
for(int i=0; i < length; ++i) {
seconds.write(secs);
nanos.write(nano);
@@ -1756,14 +1754,11 @@ public class WriterImpl implements Writer, MemoryManager.Callback {
} else {
for(int i=0; i < length; ++i) {
if (vec.noNulls || !vec.isNull[i + offset]) {
- long secs = vec.getEpochSeconds(i + offset) - base_timestamp;
- long millis = vec.getEpochMilliseconds(i + offset);
- int adjustedNanos = vec.getSignedNanos(i + offset);
- if (adjustedNanos < 0) {
- adjustedNanos += NANOS_PER_SECOND;
- }
+ val = vec.asScratchTimestamp(i + offset);
+ long millis = val.getTime();
+ long secs = millis / MILLIS_PER_SECOND - base_timestamp;
seconds.write(secs);
- nanos.write(formatNanos(adjustedNanos));
+ nanos.write(formatNanos(val.getNanos()));
indexStatistics.updateTimestamp(millis);
if (createBloomFilter) {
bloomFilter.addLong(millis);
http://git-wip-us.apache.org/repos/asf/hive/blob/ca11c393/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticIntervalYearMonthColumn.txt
----------------------------------------------------------------------
diff --git a/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticIntervalYearMonthColumn.txt b/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticIntervalYearMonthColumn.txt
index 845bc5f..c3d8d7e 100644
--- a/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticIntervalYearMonthColumn.txt
+++ b/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticIntervalYearMonthColumn.txt
@@ -18,15 +18,18 @@
package org.apache.hadoop.hive.ql.exec.vector.expressions.gen;
+import java.sql.Date;
+import org.apache.hadoop.hive.common.type.HiveIntervalYearMonth;
import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression;
import org.apache.hadoop.hive.ql.exec.vector.expressions.NullUtil;
import org.apache.hadoop.hive.ql.exec.vector.*;
import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch;
import org.apache.hadoop.hive.ql.exec.vector.VectorExpressionDescriptor;
import org.apache.hadoop.hive.ql.util.DateTimeMath;
+import org.apache.hadoop.hive.serde2.io.DateWritable;
/**
- * Generated from template DateColumnArithmeticIntervalYearMonthColumn.txt, which covers binary arithmetic
+ * Generated from template DateColumnArithmeticIntervalYearMonthColumn.txt, which covers binary arithmetic
* expressions between date and interval year month columns.
*/
public class <ClassName> extends VectorExpression {
@@ -36,12 +39,18 @@ public class <ClassName> extends VectorExpression {
private int colNum1;
private int colNum2;
private int outputColumn;
+ private Date scratchDate1;
+ private HiveIntervalYearMonth scratchIntervalYearMonth2;
+ private Date outputDate;
private DateTimeMath dtm = new DateTimeMath();
public <ClassName>(int colNum1, int colNum2, int outputColumn) {
this.colNum1 = colNum1;
this.colNum2 = colNum2;
this.outputColumn = outputColumn;
+ scratchDate1 = new Date(0);
+ scratchIntervalYearMonth2 = new HiveIntervalYearMonth();
+ outputDate = new Date(0);
}
public <ClassName>() {
@@ -54,10 +63,10 @@ public class <ClassName> extends VectorExpression {
super.evaluateChildren(batch);
}
- // Input #1 is type date (epochDays).
+ // Input #1 is type date.
LongColumnVector inputColVector1 = (LongColumnVector) batch.cols[colNum1];
- // Input #2 is type interval_year_month (months).
+ // Input #2 is type interval_year_month.
LongColumnVector inputColVector2 = (LongColumnVector) batch.cols[colNum2];
// Output is type date.
@@ -89,38 +98,65 @@ public class <ClassName> extends VectorExpression {
* conditional checks in the inner loop.
*/
if (inputColVector1.isRepeating && inputColVector2.isRepeating) {
- outputVector[0] = dtm.addMonthsToDays(vector1[0], <OperatorSymbol> (int) vector2[0]);
+ scratchDate1.setTime(DateWritable.daysToMillis((int) vector1[0]));
+ scratchIntervalYearMonth2.set((int) vector2[0]);
+ dtm.<OperatorMethod>(
+ scratchDate1, scratchIntervalYearMonth2, outputDate);
+ outputVector[0] = DateWritable.dateToDays(outputDate);
} else if (inputColVector1.isRepeating) {
+ scratchDate1.setTime(DateWritable.daysToMillis((int) vector1[0]));
if (batch.selectedInUse) {
for(int j = 0; j != n; j++) {
int i = sel[j];
- outputVector[i] = dtm.addMonthsToDays(vector1[0], <OperatorSymbol> (int) vector2[i]);
+ scratchIntervalYearMonth2.set((int) vector2[i]);
+ dtm.<OperatorMethod>(
+ scratchDate1, scratchIntervalYearMonth2, outputDate);
+ outputVector[i] = DateWritable.dateToDays(outputDate);
}
} else {
for(int i = 0; i != n; i++) {
- outputVector[i] = dtm.addMonthsToDays(vector1[0], <OperatorSymbol> (int) vector2[i]);
+ scratchIntervalYearMonth2.set((int) vector2[i]);
+ dtm.<OperatorMethod>(
+ scratchDate1, scratchIntervalYearMonth2, outputDate);
+ outputVector[i] = DateWritable.dateToDays(outputDate);
}
}
} else if (inputColVector2.isRepeating) {
if (batch.selectedInUse) {
for(int j = 0; j != n; j++) {
int i = sel[j];
- outputVector[i] = dtm.addMonthsToDays(vector1[i], <OperatorSymbol> (int) vector2[0]);
+ scratchDate1.setTime(DateWritable.daysToMillis((int) vector1[i]));
+ scratchIntervalYearMonth2.set((int) vector2[i]);
+ dtm.<OperatorMethod>(
+ scratchDate1, scratchIntervalYearMonth2, outputDate);
+ outputVector[i] = DateWritable.dateToDays(outputDate);
}
} else {
for(int i = 0; i != n; i++) {
- outputVector[i] = dtm.addMonthsToDays(vector1[i], <OperatorSymbol> (int) vector2[0]);
+ scratchDate1.setTime(DateWritable.daysToMillis((int) vector1[i]));
+ scratchIntervalYearMonth2.set((int) vector2[i]);
+ dtm.<OperatorMethod>(
+ scratchDate1, scratchIntervalYearMonth2, outputDate);
+ outputVector[i] = DateWritable.dateToDays(outputDate);
}
}
} else {
if (batch.selectedInUse) {
for(int j = 0; j != n; j++) {
int i = sel[j];
- outputVector[i] = dtm.addMonthsToDays(vector1[i], <OperatorSymbol> (int) vector2[i]);
+ scratchDate1.setTime(DateWritable.daysToMillis((int) vector1[i]));
+ scratchIntervalYearMonth2.set((int) vector2[i]);
+ dtm.<OperatorMethod>(
+ scratchDate1, scratchIntervalYearMonth2, outputDate);
+ outputVector[i] = DateWritable.dateToDays(outputDate);
}
} else {
for(int i = 0; i != n; i++) {
- outputVector[i] = dtm.addMonthsToDays(vector1[i], <OperatorSymbol> (int) vector2[i]);
+ scratchDate1.setTime(DateWritable.daysToMillis((int) vector1[i]));
+ scratchIntervalYearMonth2.set((int) vector2[i]);
+ dtm.<OperatorMethod>(
+ scratchDate1, scratchIntervalYearMonth2, outputDate);
+ outputVector[i] = DateWritable.dateToDays(outputDate);
}
}
}
http://git-wip-us.apache.org/repos/asf/hive/blob/ca11c393/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticIntervalYearMonthScalar.txt
----------------------------------------------------------------------
diff --git a/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticIntervalYearMonthScalar.txt b/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticIntervalYearMonthScalar.txt
index 86a95c9..d1474fb 100644
--- a/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticIntervalYearMonthScalar.txt
+++ b/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticIntervalYearMonthScalar.txt
@@ -18,6 +18,8 @@
package org.apache.hadoop.hive.ql.exec.vector.expressions.gen;
+import java.sql.Date;
+import org.apache.hadoop.hive.common.type.HiveIntervalYearMonth;
import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression;
import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector;
import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch;
@@ -25,6 +27,7 @@ import org.apache.hadoop.hive.ql.exec.vector.expressions.NullUtil;
import org.apache.hadoop.hive.ql.exec.vector.VectorExpressionDescriptor;
import org.apache.hadoop.hive.ql.exec.vector.*;
import org.apache.hadoop.hive.ql.util.DateTimeMath;
+import org.apache.hadoop.hive.serde2.io.DateWritable;
/**
* Generated from template DateColumnArithmeticIntervalYearMonthScalar.txt, which covers binary arithmetic
@@ -35,14 +38,18 @@ public class <ClassName> extends VectorExpression {
private static final long serialVersionUID = 1L;
private int colNum;
- private long value;
+ private HiveIntervalYearMonth value;
private int outputColumn;
+ private Date scratchDate1;
+ private Date outputDate;
private DateTimeMath dtm = new DateTimeMath();
public <ClassName>(int colNum, long value, int outputColumn) {
this.colNum = colNum;
- this.value = value;
+ this.value = new HiveIntervalYearMonth((int) value);
this.outputColumn = outputColumn;
+ scratchDate1 = new Date(0);
+ outputDate = new Date(0);
}
public <ClassName>() {
@@ -55,19 +62,19 @@ public class <ClassName> extends VectorExpression {
super.evaluateChildren(batch);
}
- // Input #1 is type date (epochDays).
- LongColumnVector inputColVector = (LongColumnVector) batch.cols[colNum];
+ // Input #1 is type date.
+ LongColumnVector inputColVector1 = (LongColumnVector) batch.cols[colNum];
// Output is type date.
LongColumnVector outputColVector = (LongColumnVector) batch.cols[outputColumn];
int[] sel = batch.selected;
- boolean[] inputIsNull = inputColVector.isNull;
+ boolean[] inputIsNull = inputColVector1.isNull;
boolean[] outputIsNull = outputColVector.isNull;
- outputColVector.noNulls = inputColVector.noNulls;
- outputColVector.isRepeating = inputColVector.isRepeating;
+ outputColVector.noNulls = inputColVector1.noNulls;
+ outputColVector.isRepeating = inputColVector1.isRepeating;
int n = batch.size;
- long[] vector = inputColVector.vector;
+ long[] vector1 = inputColVector1.vector;
long[] outputVector = outputColVector.vector;
// return immediately if batch is empty
@@ -75,32 +82,46 @@ public class <ClassName> extends VectorExpression {
return;
}
- if (inputColVector.isRepeating) {
- outputVector[0] = dtm.addMonthsToDays(vector[0], <OperatorSymbol> (int) value);
-
- // Even if there are no nulls, we always copy over entry 0. Simplifies code.
+ if (inputColVector1.isRepeating) {
+ scratchDate1.setTime(DateWritable.daysToMillis((int) vector1[0]));
+ dtm.<OperatorMethod>(
+ scratchDate1, value, outputDate);
+ outputVector[0] = DateWritable.dateToDays(outputDate);
+ // Even if there are no nulls, we always copy over entry 0. Simplifies code.
outputIsNull[0] = inputIsNull[0];
- } else if (inputColVector.noNulls) {
+ } else if (inputColVector1.noNulls) {
if (batch.selectedInUse) {
for(int j = 0; j != n; j++) {
int i = sel[j];
- outputVector[i] = dtm.addMonthsToDays(vector[i], <OperatorSymbol> (int) value);
+ scratchDate1.setTime(DateWritable.daysToMillis((int) vector1[i]));
+ dtm.<OperatorMethod>(
+ scratchDate1, value, outputDate);
+ outputVector[i] = DateWritable.dateToDays(outputDate);
}
} else {
for(int i = 0; i != n; i++) {
- outputVector[i] = dtm.addMonthsToDays(vector[i], <OperatorSymbol> (int) value);
+ scratchDate1.setTime(DateWritable.daysToMillis((int) vector1[i]));
+ dtm.<OperatorMethod>(
+ scratchDate1, value, outputDate);
+ outputVector[i] = DateWritable.dateToDays(outputDate);
}
}
} else /* there are nulls */ {
if (batch.selectedInUse) {
for(int j = 0; j != n; j++) {
int i = sel[j];
- outputVector[i] = dtm.addMonthsToDays(vector[i], <OperatorSymbol> (int) value);
+ scratchDate1.setTime(DateWritable.daysToMillis((int) vector1[i]));
+ dtm.<OperatorMethod>(
+ scratchDate1, value, outputDate);
+ outputVector[i] = DateWritable.dateToDays(outputDate);
outputIsNull[i] = inputIsNull[i];
}
} else {
for(int i = 0; i != n; i++) {
- outputVector[i] = dtm.addMonthsToDays(vector[i], <OperatorSymbol> (int) value);
+ scratchDate1.setTime(DateWritable.daysToMillis((int) vector1[i]));
+ dtm.<OperatorMethod>(
+ scratchDate1, value, outputDate);
+ outputVector[i] = DateWritable.dateToDays(outputDate);
}
System.arraycopy(inputIsNull, 0, outputIsNull, 0, n);
}
http://git-wip-us.apache.org/repos/asf/hive/blob/ca11c393/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticTimestampColumn.txt
----------------------------------------------------------------------
diff --git a/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticTimestampColumn.txt b/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticTimestampColumn.txt
index 6241ee2..63cebaf 100644
--- a/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticTimestampColumn.txt
+++ b/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticTimestampColumn.txt
@@ -18,28 +18,155 @@
package org.apache.hadoop.hive.ql.exec.vector.expressions.gen;
-import org.apache.hadoop.hive.common.type.PisaTimestamp;
+import java.sql.Timestamp;
+
+import org.apache.hadoop.hive.common.type.HiveIntervalDayTime;
import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression;
import org.apache.hadoop.hive.ql.exec.vector.expressions.NullUtil;
import org.apache.hadoop.hive.ql.exec.vector.*;
import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch;
import org.apache.hadoop.hive.ql.exec.vector.VectorExpressionDescriptor;
import org.apache.hadoop.hive.ql.util.DateTimeMath;
+import org.apache.hadoop.hive.serde2.io.DateWritable;
/**
- * Generated from template DateColumnArithmeticTimestampColumn.txt, which covers binary arithmetic
- * expressions between a date column and a timestamp column.
+ * Generated from template DateColumnArithmeticTimestampColumn.txt, a class
+ * which covers binary arithmetic expressions between a date column and timestamp column.
*/
-public class <ClassName> extends <BaseClassName> {
+public class <ClassName> extends VectorExpression {
private static final long serialVersionUID = 1L;
+ private int colNum1;
+ private int colNum2;
+ private int outputColumn;
+ private Timestamp scratchTimestamp1;
+ private DateTimeMath dtm = new DateTimeMath();
+
public <ClassName>(int colNum1, int colNum2, int outputColumn) {
- super(colNum1, colNum2, outputColumn);
+ this.colNum1 = colNum1;
+ this.colNum2 = colNum2;
+ this.outputColumn = outputColumn;
+ scratchTimestamp1 = new Timestamp(0);
}
public <ClassName>() {
- super();
+ }
+
+ @Override
+ public void evaluate(VectorizedRowBatch batch) {
+
+ if (childExpressions != null) {
+ super.evaluateChildren(batch);
+ }
+
+ // Input #1 is type Date (days). For the math we convert it to a timestamp.
+ LongColumnVector inputColVector1 = (LongColumnVector) batch.cols[colNum1];
+
+ // Input #2 is type <OperandType2>.
+ <InputColumnVectorType2> inputColVector2 = (<InputColumnVectorType2>) batch.cols[colNum2];
+
+ // Output is type <ReturnType>.
+ <OutputColumnVectorType> outputColVector = (<OutputColumnVectorType>) batch.cols[outputColumn];
+
+ int[] sel = batch.selected;
+ int n = batch.size;
+ long[] vector1 = inputColVector1.vector;
+
+ // return immediately if batch is empty
+ if (n == 0) {
+ return;
+ }
+
+ outputColVector.isRepeating =
+ inputColVector1.isRepeating && inputColVector2.isRepeating
+ || inputColVector1.isRepeating && !inputColVector1.noNulls && inputColVector1.isNull[0]
+ || inputColVector2.isRepeating && !inputColVector2.noNulls && inputColVector2.isNull[0];
+
+ // Handle nulls first
+ NullUtil.propagateNullsColCol(
+ inputColVector1, inputColVector2, outputColVector, sel, n, batch.selectedInUse);
+
+ /* Disregard nulls for processing. In other words,
+ * the arithmetic operation is performed even if one or
+ * more inputs are null. This is to improve speed by avoiding
+ * conditional checks in the inner loop.
+ */
+ if (inputColVector1.isRepeating && inputColVector2.isRepeating) {
+ scratchTimestamp1.setTime(DateWritable.daysToMillis((int) vector1[0]));
+ dtm.<OperatorMethod>(
+ scratchTimestamp1, inputColVector2.asScratch<CamelOperandType2>(0), outputColVector.getScratch<CamelReturnType>());
+ outputColVector.setFromScratch<CamelReturnType>(0);
+ } else if (inputColVector1.isRepeating) {
+ scratchTimestamp1.setTime(DateWritable.daysToMillis((int) vector1[0]));
+ if (batch.selectedInUse) {
+ for(int j = 0; j != n; j++) {
+ int i = sel[j];
+ dtm.<OperatorMethod>(
+ scratchTimestamp1, inputColVector2.asScratch<CamelOperandType2>(i), outputColVector.getScratch<CamelReturnType>());
+ outputColVector.setFromScratch<CamelReturnType>(i);
+ }
+ } else {
+ for(int i = 0; i != n; i++) {
+ dtm.<OperatorMethod>(
+ scratchTimestamp1, inputColVector2.asScratch<CamelOperandType2>(i), outputColVector.getScratch<CamelReturnType>());
+ outputColVector.setFromScratch<CamelReturnType>(i);
+ }
+ }
+ } else if (inputColVector2.isRepeating) {
+ <HiveOperandType2> value2 = inputColVector2.asScratch<CamelOperandType2>(0);
+ if (batch.selectedInUse) {
+ for(int j = 0; j != n; j++) {
+ int i = sel[j];
+ scratchTimestamp1.setTime(DateWritable.daysToMillis((int) vector1[i]));
+ dtm.<OperatorMethod>(
+ scratchTimestamp1, value2, outputColVector.getScratch<CamelReturnType>());
+ outputColVector.setFromScratch<CamelReturnType>(i);
+ }
+ } else {
+ for(int i = 0; i != n; i++) {
+ scratchTimestamp1.setTime(DateWritable.daysToMillis((int) vector1[i]));
+ dtm.<OperatorMethod>(
+ scratchTimestamp1, value2, outputColVector.getScratch<CamelReturnType>());
+ outputColVector.setFromScratch<CamelReturnType>(i);
+ }
+ }
+ } else {
+ if (batch.selectedInUse) {
+ for(int j = 0; j != n; j++) {
+ int i = sel[j];
+ scratchTimestamp1.setTime(DateWritable.daysToMillis((int) vector1[i]));
+ dtm.<OperatorMethod>(
+ scratchTimestamp1, inputColVector2.asScratch<CamelOperandType2>(i), outputColVector.getScratch<CamelReturnType>());
+ outputColVector.setFromScratch<CamelReturnType>(i);
+ }
+ } else {
+ for(int i = 0; i != n; i++) {
+ scratchTimestamp1.setTime(DateWritable.daysToMillis((int) vector1[i]));
+ dtm.<OperatorMethod>(
+ scratchTimestamp1, inputColVector2.asScratch<CamelOperandType2>(i), outputColVector.getScratch<CamelReturnType>());
+ outputColVector.setFromScratch<CamelReturnType>(i);
+ }
+ }
+ }
+
+ /* For the case when the output can have null values, follow
+ * the convention that the data values must be 1 for long and
+ * NaN for double. This is to prevent possible later zero-divide errors
+ * in complex arithmetic expressions like col2 / (col1 - 1)
+ * in the case when some col1 entries are null.
+ */
+ NullUtil.setNullDataEntries<CamelReturnType>(outputColVector, batch.selectedInUse, sel, n);
+ }
+
+ @Override
+ public int getOutputColumn() {
+ return outputColumn;
+ }
+
+ @Override
+ public String getOutputType() {
+ return "<ReturnType>";
}
@Override
@@ -49,7 +176,7 @@ public class <ClassName> extends <BaseClassName> {
VectorExpressionDescriptor.Mode.PROJECTION)
.setNumArguments(2)
.setArgumentTypes(
- VectorExpressionDescriptor.ArgumentType.getType("<OperandType1>"),
+ VectorExpressionDescriptor.ArgumentType.getType("date"),
VectorExpressionDescriptor.ArgumentType.getType("<OperandType2>"))
.setInputExpressionTypes(
VectorExpressionDescriptor.InputExpressionType.COLUMN,
http://git-wip-us.apache.org/repos/asf/hive/blob/ca11c393/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticTimestampColumnBase.txt
----------------------------------------------------------------------
diff --git a/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticTimestampColumnBase.txt b/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticTimestampColumnBase.txt
deleted file mode 100644
index a61b769..0000000
--- a/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticTimestampColumnBase.txt
+++ /dev/null
@@ -1,171 +0,0 @@
-/**
- * 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.hadoop.hive.ql.exec.vector.expressions.gen;
-
-import org.apache.hadoop.hive.common.type.PisaTimestamp;
-import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression;
-import org.apache.hadoop.hive.ql.exec.vector.expressions.NullUtil;
-import org.apache.hadoop.hive.ql.exec.vector.*;
-import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch;
-import org.apache.hadoop.hive.ql.exec.vector.VectorExpressionDescriptor;
-import org.apache.hadoop.hive.serde2.io.DateWritable;
-
-/**
- * Generated from template DateColumnArithmeticTimestampColumnBase.txt, a base class
- * which covers binary arithmetic expressions between a date column and timestamp column.
- */
-public abstract class <BaseClassName> extends VectorExpression {
-
- private static final long serialVersionUID = 1L;
-
- private int colNum1;
- private int colNum2;
- private int outputColumn;
- private PisaTimestamp scratchPisaTimestamp;
-
- public <BaseClassName>(int colNum1, int colNum2, int outputColumn) {
- this.colNum1 = colNum1;
- this.colNum2 = colNum2;
- this.outputColumn = outputColumn;
- scratchPisaTimestamp = new PisaTimestamp();
- }
-
- public <BaseClassName>() {
- }
-
- @Override
- public void evaluate(VectorizedRowBatch batch) {
-
- if (childExpressions != null) {
- super.evaluateChildren(batch);
- }
-
- // Input #1 is type Date (epochDays).
- LongColumnVector inputColVector1 = (LongColumnVector) batch.cols[colNum1];
-
- // Input #2 is type timestamp/interval_day_time.
- TimestampColumnVector inputColVector2 = (TimestampColumnVector) batch.cols[colNum2];
-
- // Output is type timestamp.
- TimestampColumnVector outputColVector = (TimestampColumnVector) batch.cols[outputColumn];
-
- int[] sel = batch.selected;
- int n = batch.size;
- long[] vector1 = inputColVector1.vector;
-
- // return immediately if batch is empty
- if (n == 0) {
- return;
- }
-
- outputColVector.isRepeating =
- inputColVector1.isRepeating && inputColVector2.isRepeating
- || inputColVector1.isRepeating && !inputColVector1.noNulls && inputColVector1.isNull[0]
- || inputColVector2.isRepeating && !inputColVector2.noNulls && inputColVector2.isNull[0];
-
- // Handle nulls first
- NullUtil.propagateNullsColCol(
- inputColVector1, inputColVector2, outputColVector, sel, n, batch.selectedInUse);
-
- /* Disregard nulls for processing. In other words,
- * the arithmetic operation is performed even if one or
- * more inputs are null. This is to improve speed by avoiding
- * conditional checks in the inner loop.
- */
- if (inputColVector1.isRepeating && inputColVector2.isRepeating) {
- outputColVector.<OperatorMethod>(
- scratchPisaTimestamp.updateFromTimestampMilliseconds(DateWritable.daysToMillis((int) vector1[0])),
- inputColVector2.asScratchPisaTimestamp(0),
- 0);
- } else if (inputColVector1.isRepeating) {
- PisaTimestamp value1 =
- scratchPisaTimestamp.updateFromTimestampMilliseconds(DateWritable.daysToMillis((int) vector1[0]));
- if (batch.selectedInUse) {
- for(int j = 0; j != n; j++) {
- int i = sel[j];
- outputColVector.<OperatorMethod>(
- value1,
- inputColVector2.asScratchPisaTimestamp(i),
- i);
- }
- } else {
- for(int i = 0; i != n; i++) {
- outputColVector.<OperatorMethod>(
- value1,
- inputColVector2.asScratchPisaTimestamp(i),
- i);
- }
- }
- } else if (inputColVector2.isRepeating) {
- PisaTimestamp value2 = inputColVector2.asScratchPisaTimestamp(0);
- if (batch.selectedInUse) {
- for(int j = 0; j != n; j++) {
- int i = sel[j];
- outputColVector.<OperatorMethod>(
- scratchPisaTimestamp.updateFromTimestampMilliseconds(DateWritable.daysToMillis((int) vector1[i])),
- value2,
- i);
- }
- } else {
- for(int i = 0; i != n; i++) {
- outputColVector.<OperatorMethod>(
- scratchPisaTimestamp.updateFromTimestampMilliseconds(DateWritable.daysToMillis((int) vector1[i])),
- value2,
- i);
- }
- }
- } else {
- if (batch.selectedInUse) {
- for(int j = 0; j != n; j++) {
- int i = sel[j];
- outputColVector.<OperatorMethod>(
- scratchPisaTimestamp.updateFromTimestampMilliseconds(DateWritable.daysToMillis((int) vector1[i])),
- inputColVector2.asScratchPisaTimestamp(i),
- i);
- }
- } else {
- for(int i = 0; i != n; i++) {
- outputColVector.<OperatorMethod>(
- scratchPisaTimestamp.updateFromTimestampMilliseconds(DateWritable.daysToMillis((int) vector1[i])),
- inputColVector2.asScratchPisaTimestamp(i),
- i);
- }
- }
- }
-
- /* For the case when the output can have null values, follow
- * the convention that the data values must be 1 for long and
- * NaN for double. This is to prevent possible later zero-divide errors
- * in complex arithmetic expressions like col2 / (col1 - 1)
- * in the case when some col1 entries are null.
- */
- NullUtil.setNullDataEntriesTimestamp(outputColVector, batch.selectedInUse, sel, n);
- }
-
- @Override
- public int getOutputColumn() {
- return outputColumn;
- }
-
- @Override
- public String getOutputType() {
- return "timestamp";
- }
-}
-
http://git-wip-us.apache.org/repos/asf/hive/blob/ca11c393/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticTimestampScalar.txt
----------------------------------------------------------------------
diff --git a/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticTimestampScalar.txt b/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticTimestampScalar.txt
index b813d11..7aee529 100644
--- a/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticTimestampScalar.txt
+++ b/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticTimestampScalar.txt
@@ -19,32 +19,123 @@
package org.apache.hadoop.hive.ql.exec.vector.expressions.gen;
import java.sql.Timestamp;
-import org.apache.hadoop.hive.common.type.PisaTimestamp;
-import org.apache.hadoop.hive.common.type.HiveIntervalDayTime;
-import org.apache.hive.common.util.DateUtils;
+import org.apache.hadoop.hive.common.type.HiveIntervalDayTime;
import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression;
-import org.apache.hadoop.hive.ql.exec.vector.TimestampColumnVector;
+import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector;
import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch;
import org.apache.hadoop.hive.ql.exec.vector.expressions.NullUtil;
import org.apache.hadoop.hive.ql.exec.vector.VectorExpressionDescriptor;
import org.apache.hadoop.hive.ql.exec.vector.*;
import org.apache.hadoop.hive.ql.util.DateTimeMath;
+import org.apache.hadoop.hive.serde2.io.DateWritable;
/**
- * Generated from template DateColumnArithmeticTimestampScalar.txt, which covers binary arithmetic
- * expressions between a date column and a timestamp scalar.
+ * Generated from template DateColumnArithmeticTimestampScalarBase.txt, a base class
+ * which covers binary arithmetic expressions between a date column and a timestamp scalar.
*/
-public class <ClassName> extends <BaseClassName> {
+public class <ClassName> extends VectorExpression {
private static final long serialVersionUID = 1L;
- public <ClassName>(int colNum, <ScalarHiveTimestampType2> value, int outputColumn) {
- super(colNum, <PisaTimestampConversion2>, outputColumn);
+ private int colNum;
+ private <HiveOperandType2> value;
+ private int outputColumn;
+ private Timestamp scratchTimestamp1;
+ private DateTimeMath dtm = new DateTimeMath();
+
+ public <ClassName>(int colNum, <HiveOperandType2> value, int outputColumn) {
+ this.colNum = colNum;
+ this.value = value;
+ this.outputColumn = outputColumn;
+ scratchTimestamp1 = new Timestamp(0);
}
public <ClassName>() {
- super();
+ }
+
+ @Override
+ public void evaluate(VectorizedRowBatch batch) {
+
+ if (childExpressions != null) {
+ super.evaluateChildren(batch);
+ }
+
+ // Input #1 is type date (days). For the math we convert it to a timestamp.
+ LongColumnVector inputColVector1 = (LongColumnVector) batch.cols[colNum];
+
+ // Output is type <ReturnType>.
+ <OutputColumnVectorType> outputColVector = (<OutputColumnVectorType>) batch.cols[outputColumn];
+
+ int[] sel = batch.selected;
+ boolean[] inputIsNull = inputColVector1.isNull;
+ boolean[] outputIsNull = outputColVector.isNull;
+ outputColVector.noNulls = inputColVector1.noNulls;
+ outputColVector.isRepeating = inputColVector1.isRepeating;
+ int n = batch.size;
+ long[] vector1 = inputColVector1.vector;
+
+ // return immediately if batch is empty
+ if (n == 0) {
+ return;
+ }
+
+ if (inputColVector1.isRepeating) {
+ scratchTimestamp1.setTime(DateWritable.daysToMillis((int) vector1[0]));
+ dtm.<OperatorMethod>(
+ scratchTimestamp1, value, outputColVector.getScratch<CamelReturnType>());
+ outputColVector.setFromScratch<CamelReturnType>(0);
+ // Even if there are no nulls, we always copy over entry 0. Simplifies code.
+ outputIsNull[0] = inputIsNull[0];
+ } else if (inputColVector1.noNulls) {
+ if (batch.selectedInUse) {
+ for(int j = 0; j != n; j++) {
+ int i = sel[j];
+ scratchTimestamp1.setTime(DateWritable.daysToMillis((int) vector1[i]));
+ dtm.<OperatorMethod>(
+ scratchTimestamp1, value, outputColVector.getScratch<CamelReturnType>());
+ outputColVector.setFromScratch<CamelReturnType>(i);
+ }
+ } else {
+ for(int i = 0; i != n; i++) {
+ scratchTimestamp1.setTime(DateWritable.daysToMillis((int) vector1[i]));
+ dtm.<OperatorMethod>(
+ scratchTimestamp1, value, outputColVector.getScratch<CamelReturnType>());
+ outputColVector.setFromScratch<CamelReturnType>(i);
+ }
+ }
+ } else /* there are nulls */ {
+ if (batch.selectedInUse) {
+ for(int j = 0; j != n; j++) {
+ int i = sel[j];
+ scratchTimestamp1.setTime(DateWritable.daysToMillis((int) vector1[i]));
+ dtm.<OperatorMethod>(
+ scratchTimestamp1, value, outputColVector.getScratch<CamelReturnType>());
+ outputColVector.setFromScratch<CamelReturnType>(i);
+ outputIsNull[i] = inputIsNull[i];
+ }
+ } else {
+ for(int i = 0; i != n; i++) {
+ scratchTimestamp1.setTime(DateWritable.daysToMillis((int) vector1[i]));
+ dtm.<OperatorMethod>(
+ scratchTimestamp1, value, outputColVector.getScratch<CamelReturnType>());
+ outputColVector.setFromScratch<CamelReturnType>(i);
+ }
+ System.arraycopy(inputIsNull, 0, outputIsNull, 0, n);
+ }
+ }
+
+ NullUtil.setNullOutputEntriesColScalar(outputColVector, batch.selectedInUse, sel, n);
+ }
+
+ @Override
+ public int getOutputColumn() {
+ return outputColumn;
+ }
+
+ @Override
+ public String getOutputType() {
+ return "<ReturnType>";
}
@Override
@@ -54,7 +145,7 @@ public class <ClassName> extends <BaseClassName> {
VectorExpressionDescriptor.Mode.PROJECTION)
.setNumArguments(2)
.setArgumentTypes(
- VectorExpressionDescriptor.ArgumentType.getType("<OperandType1>"),
+ VectorExpressionDescriptor.ArgumentType.getType("date"),
VectorExpressionDescriptor.ArgumentType.getType("<OperandType2>"))
.setInputExpressionTypes(
VectorExpressionDescriptor.InputExpressionType.COLUMN,
http://git-wip-us.apache.org/repos/asf/hive/blob/ca11c393/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticTimestampScalarBase.txt
----------------------------------------------------------------------
diff --git a/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticTimestampScalarBase.txt b/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticTimestampScalarBase.txt
deleted file mode 100644
index d64fba0..0000000
--- a/ql/src/gen/vectorization/ExpressionTemplates/DateColumnArithmeticTimestampScalarBase.txt
+++ /dev/null
@@ -1,137 +0,0 @@
-/**
- * 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.hadoop.hive.ql.exec.vector.expressions.gen;
-
-import org.apache.hadoop.hive.common.type.PisaTimestamp;
-import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression;
-import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector;
-import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch;
-import org.apache.hadoop.hive.ql.exec.vector.expressions.NullUtil;
-import org.apache.hadoop.hive.ql.exec.vector.VectorExpressionDescriptor;
-import org.apache.hadoop.hive.ql.exec.vector.*;
-import org.apache.hadoop.hive.serde2.io.DateWritable;
-
-/**
- * Generated from template DateColumnArithmeticTimestampScalarBase.txt, a base class
- * which covers binary arithmetic expressions between a date column and a timestamp scalar.
- */
-public abstract class <BaseClassName> extends VectorExpression {
-
- private static final long serialVersionUID = 1L;
-
- private int colNum;
- private PisaTimestamp value;
- private int outputColumn;
- private PisaTimestamp scratchPisaTimestamp;
-
- public <BaseClassName>(int colNum, PisaTimestamp value, int outputColumn) {
- this.colNum = colNum;
- this.value = value;
- this.outputColumn = outputColumn;
- scratchPisaTimestamp = new PisaTimestamp();
- }
-
- public <BaseClassName>() {
- }
-
- @Override
- public void evaluate(VectorizedRowBatch batch) {
-
- if (childExpressions != null) {
- super.evaluateChildren(batch);
- }
-
- // Input #1 is type date (epochDays).
- LongColumnVector inputColVector1 = (LongColumnVector) batch.cols[colNum];
-
- // Output is type timestamp.
- TimestampColumnVector outputColVector = (TimestampColumnVector) batch.cols[outputColumn];
-
- int[] sel = batch.selected;
- boolean[] inputIsNull = inputColVector1.isNull;
- boolean[] outputIsNull = outputColVector.isNull;
- outputColVector.noNulls = inputColVector1.noNulls;
- outputColVector.isRepeating = inputColVector1.isRepeating;
- int n = batch.size;
- long[] vector1 = inputColVector1.vector;
-
- // return immediately if batch is empty
- if (n == 0) {
- return;
- }
-
- if (inputColVector1.isRepeating) {
- outputColVector.<OperatorMethod>(
- scratchPisaTimestamp.updateFromTimestampMilliseconds(DateWritable.daysToMillis((int) vector1[0])),
- value,
- 0);
-
- // Even if there are no nulls, we always copy over entry 0. Simplifies code.
- outputIsNull[0] = inputIsNull[0];
- } else if (inputColVector1.noNulls) {
- if (batch.selectedInUse) {
- for(int j = 0; j != n; j++) {
- int i = sel[j];
- outputColVector.<OperatorMethod>(
- scratchPisaTimestamp.updateFromTimestampMilliseconds(DateWritable.daysToMillis((int) vector1[i])),
- value,
- i);
- }
- } else {
- for(int i = 0; i != n; i++) {
- outputColVector.<OperatorMethod>(
- scratchPisaTimestamp.updateFromTimestampMilliseconds(DateWritable.daysToMillis((int) vector1[i])),
- value,
- i);
- }
- }
- } else /* there are nulls */ {
- if (batch.selectedInUse) {
- for(int j = 0; j != n; j++) {
- int i = sel[j];
- outputColVector.<OperatorMethod>(
- scratchPisaTimestamp.updateFromTimestampMilliseconds(DateWritable.daysToMillis((int) vector1[i])),
- value,
- i);
- outputIsNull[i] = inputIsNull[i];
- }
- } else {
- for(int i = 0; i != n; i++) {
- outputColVector.<OperatorMethod>(
- scratchPisaTimestamp.updateFromTimestampMilliseconds(DateWritable.daysToMillis((int) vector1[i])),
- value,
- i);
- }
- System.arraycopy(inputIsNull, 0, outputIsNull, 0, n);
- }
- }
-
- NullUtil.setNullOutputEntriesColScalar(outputColVector, batch.selectedInUse, sel, n);
- }
-
- @Override
- public int getOutputColumn() {
- return outputColumn;
- }
-
- @Override
- public String getOutputType() {
- return "timestamp";
- }
-}
http://git-wip-us.apache.org/repos/asf/hive/blob/ca11c393/ql/src/gen/vectorization/ExpressionTemplates/DateScalarArithmeticIntervalYearMonthColumn.txt
----------------------------------------------------------------------
diff --git a/ql/src/gen/vectorization/ExpressionTemplates/DateScalarArithmeticIntervalYearMonthColumn.txt b/ql/src/gen/vectorization/ExpressionTemplates/DateScalarArithmeticIntervalYearMonthColumn.txt
index 653565e..c68ac34 100644
--- a/ql/src/gen/vectorization/ExpressionTemplates/DateScalarArithmeticIntervalYearMonthColumn.txt
+++ b/ql/src/gen/vectorization/ExpressionTemplates/DateScalarArithmeticIntervalYearMonthColumn.txt
@@ -18,6 +18,8 @@
package org.apache.hadoop.hive.ql.exec.vector.expressions.gen;
+import java.sql.Date;
+import org.apache.hadoop.hive.common.type.HiveIntervalYearMonth;
import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression;
import org.apache.hadoop.hive.ql.exec.vector.VectorExpressionDescriptor;
import org.apache.hadoop.hive.ql.exec.vector.*;
@@ -33,6 +35,7 @@ import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector;
import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch;
import org.apache.hadoop.hive.ql.exec.vector.expressions.NullUtil;
import org.apache.hadoop.hive.ql.util.DateTimeMath;
+import org.apache.hadoop.hive.serde2.io.DateWritable;
/**
* Generated from template DateTimeScalarArithmeticIntervalYearMonthColumn.txt.
@@ -44,14 +47,18 @@ public class <ClassName> extends VectorExpression {
private static final long serialVersionUID = 1L;
private int colNum;
- private long value;
+ private Date value;
private int outputColumn;
+ private HiveIntervalYearMonth scratchIntervalYearMonth2;
+ private Date outputDate;
private DateTimeMath dtm = new DateTimeMath();
public <ClassName>(long value, int colNum, int outputColumn) {
this.colNum = colNum;
- this.value = value;
+ this.value = new Date(DateWritable.daysToMillis((int) value));
this.outputColumn = outputColumn;
+ scratchIntervalYearMonth2 = new HiveIntervalYearMonth();
+ outputDate = new Date(0);
}
public <ClassName>() {
@@ -70,18 +77,18 @@ public class <ClassName> extends VectorExpression {
}
// Input #2 is type Interval_Year_Month (months).
- LongColumnVector inputColVector = (LongColumnVector) batch.cols[colNum];
+ LongColumnVector inputColVector2 = (LongColumnVector) batch.cols[colNum];
// Output is type Date.
LongColumnVector outputColVector = (LongColumnVector) batch.cols[outputColumn];
int[] sel = batch.selected;
- boolean[] inputIsNull = inputColVector.isNull;
+ boolean[] inputIsNull = inputColVector2.isNull;
boolean[] outputIsNull = outputColVector.isNull;
- outputColVector.noNulls = inputColVector.noNulls;
- outputColVector.isRepeating = inputColVector.isRepeating;
+ outputColVector.noNulls = inputColVector2.noNulls;
+ outputColVector.isRepeating = inputColVector2.isRepeating;
int n = batch.size;
- long[] vector = inputColVector.vector;
+ long[] vector2 = inputColVector2.vector;
long[] outputVector = outputColVector.vector;
// return immediately if batch is empty
@@ -89,32 +96,46 @@ public class <ClassName> extends VectorExpression {
return;
}
- if (inputColVector.isRepeating) {
- outputVector[0] = dtm.addMonthsToDays(value, <OperatorSymbol> (int) vector[0]);
-
- // Even if there are no nulls, we always copy over entry 0. Simplifies code.
+ if (inputColVector2.isRepeating) {
+ scratchIntervalYearMonth2.set((int) vector2[0]);
+ dtm.<OperatorMethod>(
+ value, scratchIntervalYearMonth2, outputDate);
+ outputVector[0] = DateWritable.dateToDays(outputDate);
+ // Even if there are no nulls, we always copy over entry 0. Simplifies code.
outputIsNull[0] = inputIsNull[0];
- } else if (inputColVector.noNulls) {
+ } else if (inputColVector2.noNulls) {
if (batch.selectedInUse) {
for(int j = 0; j != n; j++) {
int i = sel[j];
- outputVector[i] = dtm.addMonthsToDays(value, <OperatorSymbol> (int) vector[i]);
+ scratchIntervalYearMonth2.set((int) vector2[i]);
+ dtm.<OperatorMethod>(
+ value, scratchIntervalYearMonth2, outputDate);
+ outputVector[i] = DateWritable.dateToDays(outputDate);
}
} else {
for(int i = 0; i != n; i++) {
- outputVector[i] = dtm.addMonthsToDays(value, <OperatorSymbol> (int) vector[i]);
+ scratchIntervalYearMonth2.set((int) vector2[i]);
+ dtm.<OperatorMethod>(
+ value, scratchIntervalYearMonth2, outputDate);
+ outputVector[i] = DateWritable.dateToDays(outputDate);
}
}
} else { /* there are nulls */
if (batch.selectedInUse) {
for(int j = 0; j != n; j++) {
int i = sel[j];
- outputVector[i] = dtm.addMonthsToDays(value, <OperatorSymbol> (int) vector[i]);
+ scratchIntervalYearMonth2.set((int) vector2[i]);
+ dtm.<OperatorMethod>(
+ value, scratchIntervalYearMonth2, outputDate);
+ outputVector[i] = DateWritable.dateToDays(outputDate);
outputIsNull[i] = inputIsNull[i];
}
} else {
for(int i = 0; i != n; i++) {
- outputVector[i] = dtm.addMonthsToDays(value, <OperatorSymbol> (int) vector[i]);
+ scratchIntervalYearMonth2.set((int) vector2[i]);
+ dtm.<OperatorMethod>(
+ value, scratchIntervalYearMonth2, outputDate);
+ outputVector[i] = DateWritable.dateToDays(outputDate);
}
System.arraycopy(inputIsNull, 0, outputIsNull, 0, n);
}
http://git-wip-us.apache.org/repos/asf/hive/blob/ca11c393/ql/src/gen/vectorization/ExpressionTemplates/DateScalarArithmeticTimestampColumn.txt
----------------------------------------------------------------------
diff --git a/ql/src/gen/vectorization/ExpressionTemplates/DateScalarArithmeticTimestampColumn.txt b/ql/src/gen/vectorization/ExpressionTemplates/DateScalarArithmeticTimestampColumn.txt
index e93bed5..cb6b750 100644
--- a/ql/src/gen/vectorization/ExpressionTemplates/DateScalarArithmeticTimestampColumn.txt
+++ b/ql/src/gen/vectorization/ExpressionTemplates/DateScalarArithmeticTimestampColumn.txt
@@ -18,45 +18,141 @@
package org.apache.hadoop.hive.ql.exec.vector.expressions.gen;
+import java.sql.Timestamp;
+
+import org.apache.hadoop.hive.common.type.HiveIntervalDayTime;
import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression;
import org.apache.hadoop.hive.ql.exec.vector.VectorExpressionDescriptor;
import org.apache.hadoop.hive.ql.exec.vector.*;
-import org.apache.hadoop.hive.common.type.PisaTimestamp;
/*
* Because of the templatized nature of the code, either or both
* of these ColumnVector imports may be needed. Listing both of them
* rather than using ....vectorization.*;
*/
-import org.apache.hadoop.hive.ql.exec.vector.TimestampColumnVector;
+import org.apache.hadoop.hive.ql.exec.vector.DoubleColumnVector;
+import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector;
import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch;
import org.apache.hadoop.hive.ql.exec.vector.expressions.NullUtil;
import org.apache.hadoop.hive.ql.util.DateTimeMath;
+import org.apache.hadoop.hive.serde2.io.DateWritable;
/**
- * Generated from template DateScalarArithmeticTimestampColumn.txt.
+ * Generated from template DateTimeScalarArithmeticTimestampColumnBase.txt.
* Implements a vectorized arithmetic operator with a scalar on the left and a
* column vector on the right. The result is output to an output column vector.
*/
-public class <ClassName> extends <BaseClassName> {
+public class <ClassName> extends VectorExpression {
private static final long serialVersionUID = 1L;
+ private int colNum;
+ private Timestamp value;
+ private int outputColumn;
+ private DateTimeMath dtm = new DateTimeMath();
+
public <ClassName>(long value, int colNum, int outputColumn) {
- super(value, colNum, outputColumn);
+ this.colNum = colNum;
+ // Scalar input #1 is type date (days). For the math we convert it to a timestamp.
+ this.value = new Timestamp(0);
+ this.value.setTime(DateWritable.daysToMillis((int) value));
+ this.outputColumn = outputColumn;
}
public <ClassName>() {
}
@Override
+ /**
+ * Method to evaluate scalar-column operation in vectorized fashion.
+ *
+ * @batch a package of rows with each column stored in a vector
+ */
+ public void evaluate(VectorizedRowBatch batch) {
+
+ if (childExpressions != null) {
+ super.evaluateChildren(batch);
+ }
+
+ // Input #2 is type <OperandType2>.
+ <InputColumnVectorType2> inputColVector2 = (<InputColumnVectorType2>) batch.cols[colNum];
+
+ // Output is type <ReturnType>.
+ <OutputColumnVectorType> outputColVector = (<OutputColumnVectorType>) batch.cols[outputColumn];
+
+ int[] sel = batch.selected;
+ boolean[] inputIsNull = inputColVector2.isNull;
+ boolean[] outputIsNull = outputColVector.isNull;
+ outputColVector.noNulls = inputColVector2.noNulls;
+ outputColVector.isRepeating = inputColVector2.isRepeating;
+ int n = batch.size;
+
+ // return immediately if batch is empty
+ if (n == 0) {
+ return;
+ }
+
+ if (inputColVector2.isRepeating) {
+ dtm.<OperatorMethod>(
+ value, inputColVector2.asScratch<CamelOperandType2>(0), outputColVector.getScratch<CamelReturnType>());
+ outputColVector.setFromScratch<CamelReturnType>(0);
+ // Even if there are no nulls, we always copy over entry 0. Simplifies code.
+ outputIsNull[0] = inputIsNull[0];
+ } else if (inputColVector2.noNulls) {
+ if (batch.selectedInUse) {
+ for(int j = 0; j != n; j++) {
+ int i = sel[j];
+ dtm.<OperatorMethod>(
+ value, inputColVector2.asScratch<CamelOperandType2>(i), outputColVector.getScratch<CamelReturnType>());
+ outputColVector.setFromScratch<CamelReturnType>(i);
+ }
+ } else {
+ for(int i = 0; i != n; i++) {
+ dtm.<OperatorMethod>(
+ value, inputColVector2.asScratch<CamelOperandType2>(i), outputColVector.getScratch<CamelReturnType>());
+ outputColVector.setFromScratch<CamelReturnType>(i);
+ }
+ }
+ } else { /* there are nulls */
+ if (batch.selectedInUse) {
+ for(int j = 0; j != n; j++) {
+ int i = sel[j];
+ dtm.<OperatorMethod>(
+ value, inputColVector2.asScratch<CamelOperandType2>(i), outputColVector.getScratch<CamelReturnType>());
+ outputColVector.setFromScratch<CamelReturnType>(i);
+ outputIsNull[i] = inputIsNull[i];
+ }
+ } else {
+ for(int i = 0; i != n; i++) {
+ dtm.<OperatorMethod>(
+ value, inputColVector2.asScratch<CamelOperandType2>(i), outputColVector.getScratch<CamelReturnType>());
+ outputColVector.setFromScratch<CamelReturnType>(i);
+ }
+ System.arraycopy(inputIsNull, 0, outputIsNull, 0, n);
+ }
+ }
+
+ NullUtil.setNullOutputEntriesColScalar(outputColVector, batch.selectedInUse, sel, n);
+ }
+
+ @Override
+ public int getOutputColumn() {
+ return outputColumn;
+ }
+
+ @Override
+ public String getOutputType() {
+ return "<ReturnType>";
+ }
+
+ @Override
public VectorExpressionDescriptor.Descriptor getDescriptor() {
return (new VectorExpressionDescriptor.Builder())
.setMode(
VectorExpressionDescriptor.Mode.PROJECTION)
.setNumArguments(2)
.setArgumentTypes(
- VectorExpressionDescriptor.ArgumentType.getType("<OperandType1>"),
+ VectorExpressionDescriptor.ArgumentType.getType("date"),
VectorExpressionDescriptor.ArgumentType.getType("<OperandType2>"))
.setInputExpressionTypes(
VectorExpressionDescriptor.InputExpressionType.SCALAR,