Add first tests with moto #109

requirements-dev.txt

@@ -17,3 +17,4 @@ twine~=3.1.1
 wheel~=0.34.2
 sphinx~=3.0.1
 sphinx_bootstrap_theme~=0.7.1
+moto~=1.3.14

testing/test_awswrangler/test_moto.py

@@ -0,0 +1,97 @@
+import pytest
+import boto3
+import moto
+
+import awswrangler as wr
+
+from ._utils import get_df_csv, get_df_list, ensure_data_types
+
+
+@pytest.fixture(scope="module")
+def s3():
+    with moto.mock_s3():
+        boto3.resource("s3").create_bucket(Bucket="bucket")
+        yield True
+
+
+@pytest.fixture(scope="module")
+def emr():
+    with moto.mock_emr():
+        yield True
+
+
+def test_csv(s3):
+    path = "s3://bucket/test.csv"
+    wr.s3.to_csv(df=get_df_csv(), path=path, index=False)
+    df = wr.s3.read_csv(path=path)
+    assert len(df.index) == 3
+    assert len(df.columns) == 10
+
+
+def test_parquet(s3):
+    path = "s3://bucket/test.parquet"
+    wr.s3.to_parquet(df=get_df_list(), path=path, index=False, dataset=True, partition_cols=["par0", "par1"])
+    df = wr.s3.read_parquet(path=path, dataset=True)
+    ensure_data_types(df, has_list=True)
+    assert len(df.index) == 3
+    assert len(df.columns) == 18
+
+
+def test_emr(s3, emr):
+    cluster_id = wr.emr.create_cluster(
+        cluster_name="wrangler_cluster",
+        logging_s3_path="s3://bucket/emr-logs/",
+        emr_release="emr-5.29.0",
+        subnet_id="foo",
+        emr_ec2_role="EMR_EC2_DefaultRole",
+        emr_role="EMR_DefaultRole",
+        instance_type_master="m5.xlarge",
+        instance_type_core="m5.xlarge",
+        instance_type_task="m5.xlarge",
+        instance_ebs_size_master=50,
+        instance_ebs_size_core=50,
+        instance_ebs_size_task=50,
+        instance_num_on_demand_master=1,
+        instance_num_on_demand_core=0,
+        instance_num_on_demand_task=0,
+        instance_num_spot_master=0,
+        instance_num_spot_core=0,
+        instance_num_spot_task=0,
+        spot_bid_percentage_of_on_demand_master=100,
+        spot_bid_percentage_of_on_demand_core=100,
+        spot_bid_percentage_of_on_demand_task=100,
+        spot_provisioning_timeout_master=5,
+        spot_provisioning_timeout_core=5,
+        spot_provisioning_timeout_task=5,
+        spot_timeout_to_on_demand_master=False,
+        spot_timeout_to_on_demand_core=False,
+        spot_timeout_to_on_demand_task=False,
+        python3=False,
+        spark_glue_catalog=False,
+        hive_glue_catalog=False,
+        presto_glue_catalog=False,
+        consistent_view=True,
+        consistent_view_retry_count=6,
+        consistent_view_retry_seconds=15,
+        consistent_view_table_name="EMRConsistentView",
+        bootstraps_paths=None,
+        debugging=False,
+        applications=["Hadoop", "Spark", "Ganglia", "Hive"],
+        visible_to_all_users=True,
+        key_pair_name=None,
+        spark_log_level="ERROR",
+        spark_jars_path=["s3://bucket/jars/"],
+        spark_defaults={"spark.default.parallelism": "400"},
+        maximize_resource_allocation=True,
+        keep_cluster_alive_when_no_steps=False,
+        termination_protected=False,
+        spark_pyarrow=False,
+        tags={"foo": "boo", "bar": "xoo"},
+    )
+    wr.emr.get_cluster_state(cluster_id=cluster_id)
+    steps = []
+    for cmd in ['echo "Hello"', "ls -la"]:
+        steps.append(wr.emr.build_step(name=cmd, command=cmd))
+    wr.emr.submit_steps(cluster_id=cluster_id, steps=steps)
+    wr.emr.terminate_cluster(cluster_id=cluster_id)
+    wr.s3.delete_objects("s3://bucket/emr-logs/")