diff options
| -rw-r--r-- | csit.infra.dash/app/cdash/coverage/layout.py | 2 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/coverage/tables.py | 298 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/data/data.py | 6 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/data/data.yaml | 95 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/report/layout.py | 4 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/trending/layout.py | 2 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/utils/constants.py | 23 |
7 files changed, 194 insertions, 236 deletions
diff --git a/csit.infra.dash/app/cdash/coverage/layout.py b/csit.infra.dash/app/cdash/coverage/layout.py index 03d2da7fb7..f519f5a8ac 100644 --- a/csit.infra.dash/app/cdash/coverage/layout.py +++ b/csit.infra.dash/app/cdash/coverage/layout.py @@ -92,7 +92,7 @@ class Layout: if dut == "dpdk": area = "dpdk" else: - area = "-".join(lst_test_id[3:-2]) + area = ".".join(lst_test_id[3:-2]) suite = lst_test_id[-2].replace("2n1l-", "").replace("1n1l-", "").\ replace("2n-", "") test = lst_test_id[-1] diff --git a/csit.infra.dash/app/cdash/coverage/tables.py b/csit.infra.dash/app/cdash/coverage/tables.py index a773a2280c..31b227e9a8 100644 --- a/csit.infra.dash/app/cdash/coverage/tables.py +++ b/csit.infra.dash/app/cdash/coverage/tables.py @@ -75,8 +75,10 @@ def select_coverage_data( inplace=True ) + ttype = df["test_type"].to_list()[0] + # Prepare the coverage data - def _latency(hdrh_string: str, percentile: float) -> int: + def _laten(hdrh_string: str, percentile: float) -> int: """Get latency from HDRH string for given percentile. :param hdrh_string: Encoded HDRH string. @@ -105,109 +107,118 @@ def select_coverage_data( return test_id.split(".")[-1].replace("-ndrpdr", "") cov = pd.DataFrame() - cov["suite"] = df.apply(lambda row: _get_suite(row["test_id"]), axis=1) + cov["Suite"] = df.apply(lambda row: _get_suite(row["test_id"]), axis=1) cov["Test Name"] = df.apply(lambda row: _get_test(row["test_id"]), axis=1) - cov["Throughput_Unit"] = df["result_pdr_lower_rate_unit"] - cov["Throughput_NDR"] = df.apply( - lambda row: row["result_ndr_lower_rate_value"] / 1e6, axis=1 - ) - cov["Throughput_NDR_Mbps"] = df.apply( - lambda row: row["result_ndr_lower_bandwidth_value"] /1e9, axis=1 - ) - cov["Throughput_PDR"] = \ - df.apply(lambda row: row["result_pdr_lower_rate_value"] / 1e6, axis=1) - cov["Throughput_PDR_Mbps"] = df.apply( - lambda row: row["result_pdr_lower_bandwidth_value"] /1e9, axis=1 - ) - cov["Latency Forward [us]_10% PDR_P50"] = df.apply( - lambda row: _latency(row["result_latency_forward_pdr_10_hdrh"], 50.0), - axis=1 - ) - cov["Latency Forward [us]_10% PDR_P90"] = df.apply( - lambda row: _latency(row["result_latency_forward_pdr_10_hdrh"], 90.0), - axis=1 - ) - cov["Latency Forward [us]_10% PDR_P99"] = df.apply( - lambda row: _latency(row["result_latency_forward_pdr_10_hdrh"], 99.0), - axis=1 - ) - cov["Latency Forward [us]_50% PDR_P50"] = df.apply( - lambda row: _latency(row["result_latency_forward_pdr_50_hdrh"], 50.0), - axis=1 - ) - cov["Latency Forward [us]_50% PDR_P90"] = df.apply( - lambda row: _latency(row["result_latency_forward_pdr_50_hdrh"], 90.0), - axis=1 - ) - cov["Latency Forward [us]_50% PDR_P99"] = df.apply( - lambda row: _latency(row["result_latency_forward_pdr_50_hdrh"], 99.0), - axis=1 - ) - cov["Latency Forward [us]_90% PDR_P50"] = df.apply( - lambda row: _latency(row["result_latency_forward_pdr_90_hdrh"], 50.0), - axis=1 - ) - cov["Latency Forward [us]_90% PDR_P90"] = df.apply( - lambda row: _latency(row["result_latency_forward_pdr_90_hdrh"], 90.0), - axis=1 - ) - cov["Latency Forward [us]_90% PDR_P99"] = df.apply( - lambda row: _latency(row["result_latency_forward_pdr_90_hdrh"], 99.0), - axis=1 - ) - cov["Latency Reverse [us]_10% PDR_P50"] = df.apply( - lambda row: _latency(row["result_latency_reverse_pdr_10_hdrh"], 50.0), - axis=1 - ) - cov["Latency Reverse [us]_10% PDR_P90"] = df.apply( - lambda row: _latency(row["result_latency_reverse_pdr_10_hdrh"], 90.0), - axis=1 - ) - cov["Latency Reverse [us]_10% PDR_P99"] = df.apply( - lambda row: _latency(row["result_latency_reverse_pdr_10_hdrh"], 99.0), - axis=1 - ) - cov["Latency Reverse [us]_50% PDR_P50"] = df.apply( - lambda row: _latency(row["result_latency_reverse_pdr_50_hdrh"], 50.0), - axis=1 - ) - cov["Latency Reverse [us]_50% PDR_P90"] = df.apply( - lambda row: _latency(row["result_latency_reverse_pdr_50_hdrh"], 90.0), - axis=1 - ) - cov["Latency Reverse [us]_50% PDR_P99"] = df.apply( - lambda row: _latency(row["result_latency_reverse_pdr_50_hdrh"], 99.0), - axis=1 - ) - cov["Latency Reverse [us]_90% PDR_P50"] = df.apply( - lambda row: _latency(row["result_latency_reverse_pdr_90_hdrh"], 50.0), - axis=1 - ) - cov["Latency Reverse [us]_90% PDR_P90"] = df.apply( - lambda row: _latency(row["result_latency_reverse_pdr_90_hdrh"], 90.0), - axis=1 - ) - cov["Latency Reverse [us]_90% PDR_P99"] = df.apply( - lambda row: _latency(row["result_latency_reverse_pdr_90_hdrh"], 99.0), - axis=1 - ) + + if ttype == "device": + cov = cov.assign(Result="PASS") + else: + cov["Throughput_Unit"] = df["result_pdr_lower_rate_unit"] + cov["Throughput_NDR"] = df.apply( + lambda row: row["result_ndr_lower_rate_value"] / 1e6, axis=1 + ) + cov["Throughput_NDR_Mbps"] = df.apply( + lambda row: row["result_ndr_lower_bandwidth_value"] /1e9, axis=1 + ) + cov["Throughput_PDR"] = df.apply( + lambda row: row["result_pdr_lower_rate_value"] / 1e6, axis=1 + ) + cov["Throughput_PDR_Mbps"] = df.apply( + lambda row: row["result_pdr_lower_bandwidth_value"] /1e9, axis=1 + ) + cov["Latency Forward [us]_10% PDR_P50"] = df.apply( + lambda row: _laten(row["result_latency_forward_pdr_10_hdrh"], 50.0), + axis=1 + ) + cov["Latency Forward [us]_10% PDR_P90"] = df.apply( + lambda row: _laten(row["result_latency_forward_pdr_10_hdrh"], 90.0), + axis=1 + ) + cov["Latency Forward [us]_10% PDR_P99"] = df.apply( + lambda row: _laten(row["result_latency_forward_pdr_10_hdrh"], 99.0), + axis=1 + ) + cov["Latency Forward [us]_50% PDR_P50"] = df.apply( + lambda row: _laten(row["result_latency_forward_pdr_50_hdrh"], 50.0), + axis=1 + ) + cov["Latency Forward [us]_50% PDR_P90"] = df.apply( + lambda row: _laten(row["result_latency_forward_pdr_50_hdrh"], 90.0), + axis=1 + ) + cov["Latency Forward [us]_50% PDR_P99"] = df.apply( + lambda row: _laten(row["result_latency_forward_pdr_50_hdrh"], 99.0), + axis=1 + ) + cov["Latency Forward [us]_90% PDR_P50"] = df.apply( + lambda row: _laten(row["result_latency_forward_pdr_90_hdrh"], 50.0), + axis=1 + ) + cov["Latency Forward [us]_90% PDR_P90"] = df.apply( + lambda row: _laten(row["result_latency_forward_pdr_90_hdrh"], 90.0), + axis=1 + ) + cov["Latency Forward [us]_90% PDR_P99"] = df.apply( + lambda row: _laten(row["result_latency_forward_pdr_90_hdrh"], 99.0), + axis=1 + ) + cov["Latency Reverse [us]_10% PDR_P50"] = df.apply( + lambda row: _laten(row["result_latency_reverse_pdr_10_hdrh"], 50.0), + axis=1 + ) + cov["Latency Reverse [us]_10% PDR_P90"] = df.apply( + lambda row: _laten(row["result_latency_reverse_pdr_10_hdrh"], 90.0), + axis=1 + ) + cov["Latency Reverse [us]_10% PDR_P99"] = df.apply( + lambda row: _laten(row["result_latency_reverse_pdr_10_hdrh"], 99.0), + axis=1 + ) + cov["Latency Reverse [us]_50% PDR_P50"] = df.apply( + lambda row: _laten(row["result_latency_reverse_pdr_50_hdrh"], 50.0), + axis=1 + ) + cov["Latency Reverse [us]_50% PDR_P90"] = df.apply( + lambda row: _laten(row["result_latency_reverse_pdr_50_hdrh"], 90.0), + axis=1 + ) + cov["Latency Reverse [us]_50% PDR_P99"] = df.apply( + lambda row: _laten(row["result_latency_reverse_pdr_50_hdrh"], 99.0), + axis=1 + ) + cov["Latency Reverse [us]_90% PDR_P50"] = df.apply( + lambda row: _laten(row["result_latency_reverse_pdr_90_hdrh"], 50.0), + axis=1 + ) + cov["Latency Reverse [us]_90% PDR_P90"] = df.apply( + lambda row: _laten(row["result_latency_reverse_pdr_90_hdrh"], 90.0), + axis=1 + ) + cov["Latency Reverse [us]_90% PDR_P99"] = df.apply( + lambda row: _laten(row["result_latency_reverse_pdr_90_hdrh"], 99.0), + axis=1 + ) if csv: return cov - # Split data into tabels depending on the test suite. - for suite in cov["suite"].unique().tolist(): - df_suite = pd.DataFrame(cov.loc[(cov["suite"] == suite)]) - unit = df_suite["Throughput_Unit"].tolist()[0] - df_suite.rename( - columns={ - "Throughput_NDR": f"Throughput_NDR_M{unit}", - "Throughput_PDR": f"Throughput_PDR_M{unit}" - }, - inplace=True - ) - df_suite.drop(["suite", "Throughput_Unit"], axis=1, inplace=True) + # Split data into tables depending on the test suite. + for suite in cov["Suite"].unique().tolist(): + df_suite = pd.DataFrame(cov.loc[(cov["Suite"] == suite)]) + + if ttype !="device": + unit = df_suite["Throughput_Unit"].tolist()[0] + df_suite.rename( + columns={ + "Throughput_NDR": f"Throughput_NDR_M{unit}", + "Throughput_PDR": f"Throughput_PDR_M{unit}" + }, + inplace=True + ) + df_suite.drop(["Suite", "Throughput_Unit"], axis=1, inplace=True) + l_data.append((suite, df_suite, )) + return l_data @@ -224,34 +235,59 @@ def coverage_tables(data: pd.DataFrame, selected: dict) -> list: accordion_items = list() for suite, cov_data in select_coverage_data(data, selected): - cols = list() - for idx, col in enumerate(cov_data.columns): - if idx == 0: - cols.append({ - "name": ["", "", col], + if len(cov_data.columns) == 3: # VPP Device + cols = [ + { + "name": col, "id": col, "deletable": False, "selectable": False, "type": "text" - }) - elif idx < 5: - cols.append({ - "name": col.split("_"), - "id": col, - "deletable": False, - "selectable": False, - "type": "numeric", - "format": Format(precision=2, scheme=Scheme.fixed) - }) - else: - cols.append({ - "name": col.split("_"), - "id": col, - "deletable": False, - "selectable": False, - "type": "numeric", - "format": Format(precision=0, scheme=Scheme.fixed) - }) + } for col in cov_data.columns + ] + style_cell={"textAlign": "left"} + style_cell_conditional=[ + { + "if": {"column_id": "Result"}, + "textAlign": "right" + } + ] + else: # Performance + cols = list() + for idx, col in enumerate(cov_data.columns): + if idx == 0: + cols.append({ + "name": ["", "", col], + "id": col, + "deletable": False, + "selectable": False, + "type": "text" + }) + elif idx < 5: + cols.append({ + "name": col.split("_"), + "id": col, + "deletable": False, + "selectable": False, + "type": "numeric", + "format": Format(precision=2, scheme=Scheme.fixed) + }) + else: + cols.append({ + "name": col.split("_"), + "id": col, + "deletable": False, + "selectable": False, + "type": "numeric", + "format": Format(precision=0, scheme=Scheme.fixed) + }) + style_cell={"textAlign": "right"} + style_cell_conditional=[ + { + "if": {"column_id": "Test Name"}, + "textAlign": "left" + } + ] accordion_items.append( dbc.AccordionItem( @@ -267,18 +303,14 @@ def coverage_tables(data: pd.DataFrame, selected: dict) -> list: selected_columns=[], selected_rows=[], page_action="none", - style_cell={"textAlign": "right"}, - style_cell_conditional=[{ - "if": {"column_id": "Test Name"}, - "textAlign": "left" - }] + style_cell=style_cell, + style_cell_conditional=style_cell_conditional ) ) ) - return dbc.Accordion( - children=accordion_items, - class_name="gy-2 p-0", - start_collapsed=True, - always_open=True - ) + children=accordion_items, + class_name="gy-1 p-0", + start_collapsed=True, + always_open=True + ) diff --git a/csit.infra.dash/app/cdash/data/data.py b/csit.infra.dash/app/cdash/data/data.py index c8d5907200..a0d698e2b0 100644 --- a/csit.infra.dash/app/cdash/data/data.py +++ b/csit.infra.dash/app/cdash/data/data.py @@ -122,7 +122,6 @@ class Data: def _create_dataframe_from_parquet( path, partition_filter=None, columns=None, - categories=None, validate_schema=False, last_modified_begin=None, last_modified_end=None, @@ -141,8 +140,6 @@ class Data: extracted from S3. This function MUST return a bool, True to read the partition or False to ignore it. Ignored if dataset=False. :param columns: Names of columns to read from the file(s). - :param categories: List of columns names that should be returned as - pandas.Categorical. :param validate_schema: Check that individual file schemas are all the same / compatible. Schemas within a folder prefix should all be the same. Disable if you have schemas that are different and want to @@ -156,7 +153,6 @@ class Data: :type path: Union[str, List[str]] :type partition_filter: Callable[[Dict[str, str]], bool], optional :type columns: List[str], optional - :type categories: List[str], optional :type validate_schema: bool, optional :type last_modified_begin: datetime, optional :type last_modified_end: datetime, optional @@ -177,7 +173,6 @@ class Data: use_threads=True, dataset=True, columns=columns, - categories=categories, partition_filter=partition_filter, last_modified_begin=last_modified_begin, last_modified_end=last_modified_end @@ -234,7 +229,6 @@ class Data: path=data_set["path"], partition_filter=partition_filter, columns=data_set.get("columns", None), - categories=data_set.get("categories", None), days=time_period ) diff --git a/csit.infra.dash/app/cdash/data/data.yaml b/csit.infra.dash/app/cdash/data/data.yaml index 975241b84e..8beee0bacc 100644 --- a/csit.infra.dash/app/cdash/data/data.yaml +++ b/csit.infra.dash/app/cdash/data/data.yaml @@ -7,9 +7,6 @@ - build - start_time - duration - categories: - - job - - build - data_type: trending partition: test_type partition_name: mrr @@ -28,12 +25,6 @@ - result_receive_rate_rate_stdev - result_receive_rate_rate_unit - telemetry - categories: - - job - - build - - dut_type - - dut_version - - version - data_type: trending partition: test_type partition_name: ndrpdr @@ -63,12 +54,6 @@ - result_latency_forward_pdr_10_hdrh - result_latency_forward_pdr_0_hdrh - telemetry - categories: - - job - - build - - dut_type - - dut_version - - version - data_type: trending partition: test_type partition_name: hoststack @@ -91,13 +76,6 @@ - telemetry - test_id - version - categories: - - job - - build - - dut_type - - dut_version - - tg_type - - version - data_type: iterative partition: test_type partition_name: mrr @@ -117,12 +95,6 @@ - result_receive_rate_rate_stdev - result_receive_rate_rate_unit - result_receive_rate_rate_values - categories: - - job - - build - - dut_type - - dut_version - - version - data_type: iterative partition: test_type partition_name: mrr @@ -142,12 +114,6 @@ - result_receive_rate_rate_stdev - result_receive_rate_rate_unit - result_receive_rate_rate_values - categories: - - job - - build - - dut_type - - dut_version - - version - data_type: iterative partition: test_type partition_name: mrr @@ -167,12 +133,6 @@ - result_receive_rate_rate_stdev - result_receive_rate_rate_unit - result_receive_rate_rate_values - categories: - - job - - build - - dut_type - - dut_version - - version - data_type: iterative partition: test_type partition_name: ndrpdr @@ -202,12 +162,6 @@ - result_latency_forward_pdr_50_unit - result_latency_forward_pdr_10_hdrh - result_latency_forward_pdr_0_hdrh - categories: - - job - - build - - dut_type - - dut_version - - version - data_type: iterative partition: test_type partition_name: ndrpdr @@ -237,12 +191,6 @@ - result_latency_forward_pdr_50_unit - result_latency_forward_pdr_10_hdrh - result_latency_forward_pdr_0_hdrh - categories: - - job - - build - - dut_type - - dut_version - - version - data_type: iterative partition: test_type partition_name: ndrpdr @@ -272,12 +220,6 @@ - result_latency_forward_pdr_50_unit - result_latency_forward_pdr_10_hdrh - result_latency_forward_pdr_0_hdrh - categories: - - job - - build - - dut_type - - dut_version - - version - data_type: iterative partition: test_type partition_name: hoststack @@ -301,13 +243,6 @@ - telemetry - test_id - version - categories: - - job - - build - - dut_type - - dut_version - - tg_type - - version - data_type: coverage partition: test_type partition_name: ndrpdr @@ -335,30 +270,16 @@ - result_latency_forward_pdr_90_hdrh - result_latency_forward_pdr_50_hdrh - result_latency_forward_pdr_10_hdrh - categories: +- data_type: coverage + partition: test_type + partition_name: device + release: rls2302 + path: s3://fdio-docs-s3-cloudfront-index/csit/parquet/coverage_rls2302 + columns: - job - build - dut_type - dut_version - - tg_type + - passed + - test_id - version -# - data_type: coverage -# partition: test_type -# partition_name: device -# release: rls2302 -# path: s3://fdio-docs-s3-cloudfront-index/csit/parquet/coverage_rls2302 -# columns: -# - job -# - build -# - dut_type -# - dut_version -# - start_time -# - passed -# - test_id -# - version -# categories: -# - job -# - build -# - dut_type -# - dut_version -# - version diff --git a/csit.infra.dash/app/cdash/report/layout.py b/csit.infra.dash/app/cdash/report/layout.py index 8dbaea3508..1e79b68b5e 100644 --- a/csit.infra.dash/app/cdash/report/layout.py +++ b/csit.infra.dash/app/cdash/report/layout.py @@ -125,7 +125,7 @@ class Layout: if dut == "dpdk": area = "dpdk" else: - area = "-".join(lst_test_id[3:-2]) + area = ".".join(lst_test_id[3:-2]) suite = lst_test_id[-2].replace("2n1l-", "").replace("1n1l-", "").\ replace("2n-", "") test = lst_test_id[-1] @@ -1312,7 +1312,7 @@ class Layout: Input("plot-btn-download", "n_clicks"), prevent_initial_call=True ) - def _download_trending_data(store_sel, _): + def _download_iterative_data(store_sel, _): """Download the data :param store_sel: List of tests selected by user stored in the diff --git a/csit.infra.dash/app/cdash/trending/layout.py b/csit.infra.dash/app/cdash/trending/layout.py index 411061470e..005d1dc141 100644 --- a/csit.infra.dash/app/cdash/trending/layout.py +++ b/csit.infra.dash/app/cdash/trending/layout.py @@ -119,7 +119,7 @@ class Layout: if dut == "dpdk": area = "dpdk" else: - area = "-".join(lst_test[3:-2]) + area = ".".join(lst_test[3:-2]) suite = lst_test[-2].replace("2n1l-", "").replace("1n1l-", "").\ replace("2n-", "") test = lst_test[-1] diff --git a/csit.infra.dash/app/cdash/utils/constants.py b/csit.infra.dash/app/cdash/utils/constants.py index e9c08d36e3..6ab80d0b5c 100644 --- a/csit.infra.dash/app/cdash/utils/constants.py +++ b/csit.infra.dash/app/cdash/utils/constants.py @@ -124,12 +124,23 @@ class Constants: "lb": "Load Balancer", "srv6": "SRv6 Routing", "vm_vhost": "VMs vhost-user", - "nfv_density-dcr_memif-chain_ipsec": "CNF Service Chains Routing IPSec", - "nfv_density-vm_vhost-chain_dot1qip4vxlan":"VNF Service Chains Tunnels", - "nfv_density-vm_vhost-chain": "VNF Service Chains Routing", - "nfv_density-dcr_memif-pipeline": "CNF Service Pipelines Routing", - "nfv_density-dcr_memif-chain": "CNF Service Chains Routing", - "hoststack": "Hoststack" + "nfv_density.dcr_memif.chain_ipsec": "CNF Service Chains Routing IPSec", + "nfv_density.vm_vhost.chain_dot1qip4vxlan":"VNF Service Chains Tunnels", + "nfv_density.vm_vhost.chain": "VNF Service Chains Routing", + "nfv_density.dcr_memif.pipeline": "CNF Service Pipelines Routing", + "nfv_density.dcr_memif.chain": "CNF Service Chains Routing", + "hoststack": "Hoststack", + "flow": "Flow", + "l2bd": "L2 Bridge Domain", + "crypto.ethip4": "IPSec IPv4 Routing", + "crypto.ethip6": "IPSec IPv6 Routing", + "interfaces": "Interfaces", + "ip4_tunnels.lisp": "IPv4 Tunnels LISP", + "ip6_tunnels.lisp": "IPv6 Tunnels LISP", + "l2patch": "L2 Patch", + "l2xc": "L2 Cross Connect", + "vm_vhost.ethip4": "VMs vhost-user IPv4 Routing", + "vm_vhost.ethip6": "VMs vhost-user IPv6 Routing" } # URL style. |