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path: root/src/vnet/dpo/interface_rx_dpo.c
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/*
 * Copyright (c) 2016 Cisco and/or its affiliates.
 * Licensed 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.
 */

#include <vnet/dpo/interface_rx_dpo.h>
#include <vnet/fib/fib_node.h>

/*
 * The 'DB' of interface DPOs.
 * There is only one  per-interface per-protocol, so this is a per-interface
 * vector
 */
static index_t *interface_rx_dpo_db[DPO_PROTO_NUM];

static interface_rx_dpo_t *
interface_rx_dpo_alloc (void)
{
    interface_rx_dpo_t *ido;

    pool_get(interface_rx_dpo_pool, ido);

    return (ido);
}

static inline interface_rx_dpo_t *
interface_rx_dpo_get_from_dpo (const dpo_id_t *dpo)
{
    ASSERT(DPO_INTERFACE_RX == dpo->dpoi_type);

    return (interface_rx_dpo_get(dpo->dpoi_index));
}

static inline index_t
interface_rx_dpo_get_index (interface_rx_dpo_t *ido)
{
    return (ido - interface_rx_dpo_pool);
}

static void
interface_rx_dpo_lock (dpo_id_t *dpo)
{
    interface_rx_dpo_t *ido;

    ido = interface_rx_dpo_get_from_dpo(dpo);
    ido->ido_locks++;
}

static void
interface_rx_dpo_unlock (dpo_id_t *dpo)
{
    interface_rx_dpo_t *ido;

    ido = interface_rx_dpo_get_from_dpo(dpo);
    ido->ido_locks--;

    if (0 == ido->ido_locks)
    {
        interface_rx_dpo_db[ido->ido_proto][ido->ido_sw_if_index] =
            INDEX_INVALID;
        pool_put(interface_rx_dpo_pool, ido);
    }
}

/*
 * interface_rx_dpo_add_or_lock
 *
 * Add/create and lock a new or lock an existing for the interface DPO
 * on the interface and protocol given
 */
void
interface_rx_dpo_add_or_lock (dpo_proto_t proto,
                              u32 sw_if_index,
                              dpo_id_t *dpo)
{
    interface_rx_dpo_t *ido;

    vec_validate_init_empty(interface_rx_dpo_db[proto],
                            sw_if_index,
                            INDEX_INVALID);

    if (INDEX_INVALID == interface_rx_dpo_db[proto][sw_if_index])
    {
        ido = interface_rx_dpo_alloc();

        ido->ido_sw_if_index = sw_if_index;
        ido->ido_proto = proto;

        interface_rx_dpo_db[proto][sw_if_index] =
            interface_rx_dpo_get_index(ido);
    }
    else
    {
        ido = interface_rx_dpo_get(interface_rx_dpo_db[proto][sw_if_index]);
    }

    dpo_set(dpo, DPO_INTERFACE_RX, proto, interface_rx_dpo_get_index(ido));
}


static clib_error_t *
interface_rx_dpo_interface_state_change (vnet_main_t * vnm,
                                         u32 sw_if_index,
                                         u32 flags)
{
    /*
     */
    return (NULL);
}

VNET_SW_INTERFACE_ADMIN_UP_DOWN_FUNCTION(
    interface_rx_dpo_interface_state_change);

/**
 * @brief Registered callback for HW interface state changes
 */
static clib_error_t *
interface_rx_dpo_hw_interface_state_change (vnet_main_t * vnm,
                                            u32 hw_if_index,
                                            u32 flags)
{
    return (NULL);
}

VNET_HW_INTERFACE_LINK_UP_DOWN_FUNCTION(
    interface_rx_dpo_hw_interface_state_change);

static clib_error_t *
interface_rx_dpo_interface_delete (vnet_main_t * vnm,
                                   u32 sw_if_index,
                                   u32 is_add)
{
    return (NULL);
}

VNET_SW_INTERFACE_ADD_DEL_FUNCTION(
    interface_rx_dpo_interface_delete);

u8*
format_interface_rx_dpo (u8* s, va_list *ap)
{
    index_t index = va_arg(*ap, index_t);
    CLIB_UNUSED(u32 indent) = va_arg(*ap, u32);
    vnet_main_t * vnm = vnet_get_main();
    interface_rx_dpo_t *ido = interface_rx_dpo_get(index);

    return (format(s, "%U-dpo: %U",
                   format_vnet_sw_interface_name,
                   vnm,
                   vnet_get_sw_interface(vnm, ido->ido_sw_if_index),
                   format_dpo_proto, ido->ido_proto));
}

static void
interface_rx_dpo_mem_show (void)
{
    fib_show_memory_usage("Interface",
                          pool_elts(interface_rx_dpo_pool),
                          pool_len(interface_rx_dpo_pool),
                          sizeof(interface_rx_dpo_t));
}


const static dpo_vft_t interface_rx_dpo_vft = {
    .dv_lock = interface_rx_dpo_lock,
    .dv_unlock = interface_rx_dpo_unlock,
    .dv_format = format_interface_rx_dpo,
    .dv_mem_show = interface_rx_dpo_mem_show,
};

/**
 * @brief The per-protocol VLIB graph nodes that are assigned to a glean
 *        object.
 *
 * this means that these graph nodes are ones from which a glean is the
 * parent object in the DPO-graph.
 */
const static char* const interface_rx_dpo_ip4_nodes[] =
{
    "interface-rx-dpo-ip4",
    NULL,
};
const static char* const interface_rx_dpo_ip6_nodes[] =
{
    "interface-rx-dpo-ip6",
    NULL,
};
const static char* const interface_rx_dpo_l2_nodes[] =
{
    "interface-rx-dpo-l2",
    NULL,
};

const static char* const * const interface_rx_dpo_nodes[DPO_PROTO_NUM] =
{
    [DPO_PROTO_IP4]  = interface_rx_dpo_ip4_nodes,
    [DPO_PROTO_IP6]  = interface_rx_dpo_ip6_nodes,
    [DPO_PROTO_ETHERNET]  = interface_rx_dpo_l2_nodes,
    [DPO_PROTO_MPLS] = NULL,
};

void
interface_rx_dpo_module_init (void)
{
    dpo_register(DPO_INTERFACE_RX,
                 &interface_rx_dpo_vft,
                 interface_rx_dpo_nodes);
}

/**
 * @brief Interface DPO trace data
 */
typedef struct interface_rx_dpo_trace_t_
{
    u32 sw_if_index;
} interface_rx_dpo_trace_t;

typedef enum interface_rx_dpo_next_t_
{
    INTERFACE_RX_DPO_DROP = 0,
    INTERFACE_RX_DPO_INPUT = 1,
} interface_rx_dpo_next_t;

always_inline uword
interface_rx_dpo_inline (vlib_main_t * vm,
                         vlib_node_runtime_t * node,
                         vlib_frame_t * from_frame)
{
    u32 n_left_from, next_index, * from, * to_next;
    u32 thread_index = vlib_get_thread_index ();
    vnet_interface_main_t *im;

    im = &vnet_get_main ()->interface_main;
    from = vlib_frame_vector_args (from_frame);
    n_left_from = from_frame->n_vectors;

    next_index = node->cached_next_index;

    while (n_left_from > 0)
    {
        u32 n_left_to_next;

        vlib_get_next_frame(vm, node, next_index, to_next, n_left_to_next);

        while (n_left_from >= 4 && n_left_to_next > 2)
        {
            const interface_rx_dpo_t *ido0, *ido1;
            u32 bi0, idoi0, bi1, idoi1;
            vlib_buffer_t *b0, *b1;

            bi0 = from[0];
            to_next[0] = bi0;
            bi1 = from[1];
            to_next[1] = bi1;
            from += 2;
            to_next += 2;
            n_left_from -= 2;
            n_left_to_next -= 2;

            b0 = vlib_get_buffer (vm, bi0);
            b1 = vlib_get_buffer (vm, bi1);

            idoi0 = vnet_buffer(b0)->ip.adj_index[VLIB_TX];
            idoi1 = vnet_buffer(b1)->ip.adj_index[VLIB_TX];
            ido0 = interface_rx_dpo_get(idoi0);
            ido1 = interface_rx_dpo_get(idoi1);

            vnet_buffer(b0)->sw_if_index[VLIB_RX] = ido0->ido_sw_if_index;
            vnet_buffer(b1)->sw_if_index[VLIB_RX] = ido1->ido_sw_if_index;

            vlib_increment_combined_counter (im->combined_sw_if_counters
                                             + VNET_INTERFACE_COUNTER_RX,
                                             thread_index,
                                             ido0->ido_sw_if_index,
                                             1,
                                             vlib_buffer_length_in_chain (vm, b0));
            vlib_increment_combined_counter (im->combined_sw_if_counters
                                             + VNET_INTERFACE_COUNTER_RX,
                                             thread_index,
                                             ido1->ido_sw_if_index,
                                             1,
                                             vlib_buffer_length_in_chain (vm, b1));

            if (PREDICT_FALSE(b0->flags & VLIB_BUFFER_IS_TRACED))
            {
                interface_rx_dpo_trace_t *tr0;

                tr0 = vlib_add_trace (vm, node, b0, sizeof (*tr0));
                tr0->sw_if_index = ido0->ido_sw_if_index;
            }
            if (PREDICT_FALSE(b1->flags & VLIB_BUFFER_IS_TRACED))
            {
                interface_rx_dpo_trace_t *tr1;

                tr1 = vlib_add_trace (vm, node, b1, sizeof (*tr1));
                tr1->sw_if_index = ido1->ido_sw_if_index;
            }

            vlib_validate_buffer_enqueue_x2(vm, node, next_index, to_next,
                                            n_left_to_next, bi0, bi1,
                                            INTERFACE_RX_DPO_INPUT,
                                            INTERFACE_RX_DPO_INPUT);
        }

        while (n_left_from > 0 && n_left_to_next > 0)
        {
            const interface_rx_dpo_t * ido0;
            vlib_buffer_t * b0;
            u32 bi0, idoi0;

            bi0 = from[0];
            to_next[0] = bi0;
            from += 1;
            to_next += 1;
            n_left_from -= 1;
            n_left_to_next -= 1;

            b0 = vlib_get_buffer (vm, bi0);

            idoi0 = vnet_buffer(b0)->ip.adj_index[VLIB_TX];
            ido0 = interface_rx_dpo_get(idoi0);

            /* Swap the RX interface of the packet to the one the
             * interface DPR represents */
            vnet_buffer(b0)->sw_if_index[VLIB_RX] = ido0->ido_sw_if_index;

            /* Bump the interface's RX coutners */
            vlib_increment_combined_counter (im->combined_sw_if_counters
                                             + VNET_INTERFACE_COUNTER_RX,
                                             thread_index,
                                             ido0->ido_sw_if_index,
                                             1,
                                             vlib_buffer_length_in_chain (vm, b0));

            if (PREDICT_FALSE(b0->flags & VLIB_BUFFER_IS_TRACED))
            {
                interface_rx_dpo_trace_t *tr;

                tr = vlib_add_trace (vm, node, b0, sizeof (*tr));
                tr->sw_if_index = ido0->ido_sw_if_index;
            }

            vlib_validate_buffer_enqueue_x1(vm, node, next_index, to_next,
                                            n_left_to_next, bi0,
                                            INTERFACE_RX_DPO_INPUT);
        }
        vlib_put_next_frame (vm, node, next_index, n_left_to_next);
    }
    return from_frame->n_vectors;
}

static u8 *
format_interface_rx_dpo_trace (u8 * s, va_list * args)
{
    CLIB_UNUSED (vlib_main_t * vm) = va_arg (*args, vlib_main_t *);
    CLIB_UNUSED (vlib_node_t * node) = va_arg (*args, vlib_node_t *);
    interface_rx_dpo_trace_t * t = va_arg (*args, interface_rx_dpo_trace_t *);
    u32 indent = format_get_indent (s);
    s = format (s, "%U sw_if_index:%d",
                format_white_space, indent,
                t->sw_if_index);
    return s;
}

static uword
interface_rx_dpo_ip4 (vlib_main_t * vm,
                      vlib_node_runtime_t * node,
                      vlib_frame_t * from_frame)
{
    return (interface_rx_dpo_inline(vm, node, from_frame));
}

static uword
interface_rx_dpo_ip6 (vlib_main_t * vm,
                      vlib_node_runtime_t * node,
                      vlib_frame_t * from_frame)
{
    return (interface_rx_dpo_inline(vm, node, from_frame));
}

static uword
interface_rx_dpo_l2 (vlib_main_t * vm,
                     vlib_node_runtime_t * node,
                     vlib_frame_t * from_frame)
{
    return (interface_rx_dpo_inline(vm, node, from_frame));
}

VLIB_REGISTER_NODE (interface_rx_dpo_ip4_node) = {
    .function = interface_rx_dpo_ip4,
    .name = "interface-rx-dpo-ip4",
    .vector_size = sizeof (u32),
    .format_trace = format_interface_rx_dpo_trace,

    .n_next_nodes = 2,
    .next_nodes = {
        [INTERFACE_RX_DPO_DROP] = "ip4-drop",
        [INTERFACE_RX_DPO_INPUT] = "ip4-input",
    },
};

VLIB_NODE_FUNCTION_MULTIARCH (interface_rx_dpo_ip4_node,
                              interface_rx_dpo_ip4)

VLIB_REGISTER_NODE (interface_rx_dpo_ip6_node) = {
    .function = interface_rx_dpo_ip6,
    .name = "interface-rx-dpo-ip6",
    .vector_size = sizeof (u32),
    .format_trace = format_interface_rx_dpo_trace,

    .n_next_nodes = 2,
    .next_nodes = {
        [INTERFACE_RX_DPO_DROP] = "ip6-drop",
        [INTERFACE_RX_DPO_INPUT] = "ip6-input",
    },
};

VLIB_NODE_FUNCTION_MULTIARCH (interface_rx_dpo_ip6_node,
                              interface_rx_dpo_ip6)

VLIB_REGISTER_NODE (interface_rx_dpo_l2_node) = {
    .function = interface_rx_dpo_l2,
    .name = "interface-rx-dpo-l2",
    .vector_size = sizeof (u32),
    .format_trace = format_interface_rx_dpo_trace,

    .n_next_nodes = 2,
    .next_nodes = {
        [INTERFACE_RX_DPO_DROP] = "error-drop",
        [INTERFACE_RX_DPO_INPUT] = "l2-input",
    },
};

VLIB_NODE_FUNCTION_MULTIARCH (interface_rx_dpo_l2_node,
                              interface_rx_dpo_l2)
/span> logging.info(" Writing file: '{}'".format(file_name)) with open(file_name, "w") as file_handler: file_handler.write(",".join(header) + "\n") for item in table_lst: file_handler.write(",".join(item) + "\n") logging.info(" Done.") def table_performance_improvements(table, input_data): """Generate the table(s) with algorithm: table_performance_improvements specified in the specification file. :param table: Table to generate. :param input_data: Data to process. :type table: pandas.Series :type input_data: InputData """ def _write_line_to_file(file_handler, data): """Write a line to the .csv file. :param file_handler: File handler for the csv file. It must be open for writing text. :param data: Item to be written to the file. :type file_handler: BinaryIO :type data: list """ line_lst = list() for item in data: if isinstance(item["data"], str): # Remove -?drdisc from the end if item["data"].endswith("drdisc"): item["data"] = item["data"][:-8] line_lst.append(item["data"]) elif isinstance(item["data"], float): line_lst.append("{:.1f}".format(item["data"])) elif item["data"] is None: line_lst.append("") file_handler.write(",".join(line_lst) + "\n") logging.info(" Generating the table {0} ...". format(table.get("title", ""))) # Read the template file_name = table.get("template", None) if file_name: try: tmpl = _read_csv_template(file_name) except PresentationError: logging.error(" The template '{0}' does not exist. Skipping the " "table.".format(file_name)) return None else: logging.error("The template is not defined. Skipping the table.") return None # Transform the data data = input_data.filter_data(table) # Prepare the header of the tables header = list() for column in table["columns"]: header.append(column["title"]) # Generate the data for the table according to the model in the table # specification tbl_lst = list() for tmpl_item in tmpl: tbl_item = list() for column in table["columns"]: cmd = column["data"].split(" ")[0] args = column["data"].split(" ")[1:] if cmd == "template": try: val = float(tmpl_item[int(args[0])]) except ValueError: val = tmpl_item[int(args[0])] tbl_item.append({"data": val}) elif cmd == "data": jobs = args[0:-1] operation = args[-1] data_lst = list() for job in jobs: for build in data[job]: try: data_lst.append(float(build[tmpl_item[0]] ["throughput"]["value"])) except (KeyError, TypeError): # No data, ignore continue if data_lst: tbl_item.append({"data": (eval(operation)(data_lst)) / 1000000}) else: tbl_item.append({"data": None}) elif cmd == "operation": operation = args[0] try: nr1 = float(tbl_item[int(args[1])]["data"]) nr2 = float(tbl_item[int(args[2])]["data"]) if nr1 and nr2: tbl_item.append({"data": eval(operation)(nr1, nr2)}) else: tbl_item.append({"data": None}) except (IndexError, ValueError, TypeError): logging.error("No data for {0}".format(tbl_item[0]["data"])) tbl_item.append({"data": None}) continue else: logging.error("Not supported command {0}. Skipping the table.". format(cmd)) return None tbl_lst.append(tbl_item) # Sort the table according to the relative change tbl_lst.sort(key=lambda rel: rel[-1]["data"], reverse=True) # Create the tables and write them to the files file_names = [ "{0}_ndr_top{1}".format(table["output-file"], table["output-file-ext"]), "{0}_pdr_top{1}".format(table["output-file"], table["output-file-ext"]), "{0}_ndr_low{1}".format(table["output-file"], table["output-file-ext"]), "{0}_pdr_low{1}".format(table["output-file"], table["output-file-ext"]) ] for file_name in file_names: logging.info(" Writing the file '{0}'".format(file_name)) with open(file_name, "w") as file_handler: file_handler.write(",".join(header) + "\n") for item in tbl_lst: if isinstance(item[-1]["data"], float): rel_change = round(item[-1]["data"], 1) else: rel_change = item[-1]["data"] if "ndr_top" in file_name \ and "ndr" in item[0]["data"] \ and rel_change >= 10.0: _write_line_to_file(file_handler, item) elif "pdr_top" in file_name \ and "pdr" in item[0]["data"] \ and rel_change >= 10.0: _write_line_to_file(file_handler, item) elif "ndr_low" in file_name \ and "ndr" in item[0]["data"] \ and rel_change < 10.0: _write_line_to_file(file_handler, item) elif "pdr_low" in file_name \ and "pdr" in item[0]["data"] \ and rel_change < 10.0: _write_line_to_file(file_handler, item) logging.info(" Done.") def _read_csv_template(file_name): """Read the template from a .csv file. :param file_name: Name / full path / relative path of the file to read. :type file_name: str :returns: Data from the template as list (lines) of lists (items on line). :rtype: list :raises: PresentationError if it is not possible to read the file. """ try: with open(file_name, 'r') as csv_file: tmpl_data = list() for line in csv_file: tmpl_data.append(line[:-1].split(",")) return tmpl_data except IOError as err: raise PresentationError(str(err), level="ERROR") def table_performance_comparison(table, input_data): """Generate the table(s) with algorithm: table_performance_comparison specified in the specification file. :param table: Table to generate. :param input_data: Data to process. :type table: pandas.Series :type input_data: InputData """ logging.info(" Generating the table {0} ...". format(table.get("title", ""))) # Transform the data data = input_data.filter_data(table, continue_on_error=True) # Prepare the header of the tables try: header = ["Test case", "{0} Throughput [Mpps]".format(table["reference"]["title"]), "{0} stdev [Mpps]".format(table["reference"]["title"]), "{0} Throughput [Mpps]".format(table["compare"]["title"]), "{0} stdev [Mpps]".format(table["compare"]["title"]), "Change [%]"] header_str = ",".join(header) + "\n" except (AttributeError, KeyError) as err: logging.error("The model is invalid, missing parameter: {0}". format(err)) return # Prepare data to the table: tbl_dict = dict() for job, builds in table["reference"]["data"].items(): for build in builds: for tst_name, tst_data in data[job][str(build)].iteritems(): if tbl_dict.get(tst_name, None) is None: name = "{0}-{1}".format(tst_data["parent"].split("-")[0], "-".join(tst_data["name"]. split("-")[1:])) tbl_dict[tst_name] = {"name": name, "ref-data": list(), "cmp-data": list()} try: tbl_dict[tst_name]["ref-data"].\ append(tst_data["throughput"]["value"]) except TypeError: pass # No data in output.xml for this test for job, builds in table["compare"]["data"].items(): for build in builds: for tst_name, tst_data in data[job][str(build)].iteritems(): try: tbl_dict[tst_name]["cmp-data"].\ append(tst_data["throughput"]["value"]) except KeyError: pass except TypeError: tbl_dict.pop(tst_name, None) tbl_lst = list() for tst_name in tbl_dict.keys(): item = [tbl_dict[tst_name]["name"], ] if tbl_dict[tst_name]["ref-data"]: data_t = remove_outliers(tbl_dict[tst_name]["ref-data"], table["outlier-const"]) item.append(round(mean(data_t) / 1000000, 2)) item.append(round(stdev(data_t) / 1000000, 2)) else: item.extend([None, None]) if tbl_dict[tst_name]["cmp-data"]: data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"], table["outlier-const"]) item.append(round(mean(data_t) / 1000000, 2)) item.append(round(stdev(data_t) / 1000000, 2)) else: item.extend([None, None]) if item[1] is not None and item[3] is not None: item.append(int(relative_change(float(item[1]), float(item[3])))) if len(item) == 6: tbl_lst.append(item) # Sort the table according to the relative change tbl_lst.sort(key=lambda rel: rel[-1], reverse=True) # Generate tables: # All tests in csv: tbl_names = ["{0}-ndr-1t1c-full{1}".format(table["output-file"], table["output-file-ext"]), "{0}-ndr-2t2c-full{1}".format(table["output-file"], table["output-file-ext"]), "{0}-ndr-4t4c-full{1}".format(table["output-file"], table["output-file-ext"]), "{0}-pdr-1t1c-full{1}".format(table["output-file"], table["output-file-ext"]), "{0}-pdr-2t2c-full{1}".format(table["output-file"], table["output-file-ext"]), "{0}-pdr-4t4c-full{1}".format(table["output-file"], table["output-file-ext"]) ] for file_name in tbl_names: logging.info(" Writing file: '{0}'".format(file_name)) with open(file_name, "w") as file_handler: file_handler.write(header_str) for test in tbl_lst: if (file_name.split("-")[-3] in test[0] and # NDR vs PDR file_name.split("-")[-2] in test[0]): # cores test[0] = "-".join(test[0].split("-")[:-1]) file_handler.write(",".join([str(item) for item in test]) + "\n") # All tests in txt: tbl_names_txt = ["{0}-ndr-1t1c-full.txt".format(table["output-file"]), "{0}-ndr-2t2c-full.txt".format(table["output-file"]), "{0}-ndr-4t4c-full.txt".format(table["output-file"]), "{0}-pdr-1t1c-full.txt".format(table["output-file"]), "{0}-pdr-2t2c-full.txt".format(table["output-file"]), "{0}-pdr-4t4c-full.txt".format(table["output-file"]) ] for i, txt_name in enumerate(tbl_names_txt): txt_table = None logging.info(" Writing file: '{0}'".format(txt_name)) with open(tbl_names[i], 'rb') as csv_file: csv_content = csv.reader(csv_file, delimiter=',', quotechar='"') for row in csv_content: if txt_table is None: txt_table = prettytable.PrettyTable(row) else: txt_table.add_row(row) txt_table.align["Test case"] = "l" with open(txt_name, "w") as txt_file: txt_file.write(str(txt_table)) # Selected tests in csv: input_file = "{0}-ndr-1t1c-full{1}".format(table["output-file"], table["output-file-ext"]) with open(input_file, "r") as in_file: lines = list() for line in in_file: lines.append(line) output_file = "{0}-ndr-1t1c-top{1}".format(table["output-file"], table["output-file-ext"]) logging.info(" Writing file: '{0}'".format(output_file)) with open(output_file, "w") as out_file: out_file.write(header_str) for i, line in enumerate(lines[1:]): if i == table["nr-of-tests-shown"]: break out_file.write(line) output_file = "{0}-ndr-1t1c-bottom{1}".format(table["output-file"], table["output-file-ext"]) logging.info(" Writing file: '{0}'".format(output_file)) with open(output_file, "w") as out_file: out_file.write(header_str) for i, line in enumerate(lines[-1:0:-1]): if i == table["nr-of-tests-shown"]: break out_file.write(line) input_file = "{0}-pdr-1t1c-full{1}".format(table["output-file"], table["output-file-ext"]) with open(input_file, "r") as in_file: lines = list() for line in in_file: lines.append(line) output_file = "{0}-pdr-1t1c-top{1}".format(table["output-file"], table["output-file-ext"]) logging.info(" Writing file: '{0}'".format(output_file)) with open(output_file, "w") as out_file: out_file.write(header_str) for i, line in enumerate(lines[1:]): if i == table["nr-of-tests-shown"]: break out_file.write(line) output_file = "{0}-pdr-1t1c-bottom{1}".format(table["output-file"], table["output-file-ext"]) logging.info(" Writing file: '{0}'".format(output_file)) with open(output_file, "w") as out_file: out_file.write(header_str) for i, line in enumerate(lines[-1:0:-1]): if i == table["nr-of-tests-shown"]: break out_file.write(line) def table_performance_comparison_mrr(table, input_data): """Generate the table(s) with algorithm: table_performance_comparison_mrr specified in the specification file. :param table: Table to generate. :param input_data: Data to process. :type table: pandas.Series :type input_data: InputData """ logging.info(" Generating the table {0} ...". format(table.get("title", ""))) # Transform the data data = input_data.filter_data(table, continue_on_error=True) # Prepare the header of the tables try: header = ["Test case", "{0} Throughput [Mpps]".format(table["reference"]["title"]), "{0} stdev [Mpps]".format(table["reference"]["title"]), "{0} Throughput [Mpps]".format(table["compare"]["title"]), "{0} stdev [Mpps]".format(table["compare"]["title"]), "Change [%]"] header_str = ",".join(header) + "\n" except (AttributeError, KeyError) as err: logging.error("The model is invalid, missing parameter: {0}". format(err)) return # Prepare data to the table: tbl_dict = dict() for job, builds in table["reference"]["data"].items(): for build in builds: for tst_name, tst_data in data[job][str(build)].iteritems(): if tbl_dict.get(tst_name, None) is None: name = "{0}-{1}".format(tst_data["parent"].split("-")[0], "-".join(tst_data["name"]. split("-")[1:])) tbl_dict[tst_name] = {"name": name, "ref-data": list(), "cmp-data": list()} try: tbl_dict[tst_name]["ref-data"].\ append(tst_data["result"]["throughput"]) except TypeError: pass # No data in output.xml for this test for job, builds in table["compare"]["data"].items(): for build in builds: for tst_name, tst_data in data[job][str(build)].iteritems(): try: tbl_dict[tst_name]["cmp-data"].\ append(tst_data["result"]["throughput"]) except KeyError: pass except TypeError: tbl_dict.pop(tst_name, None) tbl_lst = list() for tst_name in tbl_dict.keys(): item = [tbl_dict[tst_name]["name"], ] if tbl_dict[tst_name]["ref-data"]: data_t = remove_outliers(tbl_dict[tst_name]["ref-data"], table["outlier-const"]) item.append(round(mean(data_t) / 1000000, 2)) item.append(round(stdev(data_t) / 1000000, 2)) else: item.extend([None, None]) if tbl_dict[tst_name]["cmp-data"]: data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"], table["outlier-const"]) item.append(round(mean(data_t) / 1000000, 2)) item.append(round(stdev(data_t) / 1000000, 2)) else: item.extend([None, None]) if item[1] is not None and item[3] is not None and item[1] != 0: item.append(int(relative_change(float(item[1]), float(item[3])))) if len(item) == 6: tbl_lst.append(item) # Sort the table according to the relative change tbl_lst.sort(key=lambda rel: rel[-1], reverse=True) # Generate tables: # All tests in csv: tbl_names = ["{0}-1t1c-full{1}".format(table["output-file"], table["output-file-ext"]), "{0}-2t2c-full{1}".format(table["output-file"], table["output-file-ext"]), "{0}-4t4c-full{1}".format(table["output-file"], table["output-file-ext"]) ] for file_name in tbl_names: logging.info(" Writing file: '{0}'".format(file_name)) with open(file_name, "w") as file_handler: file_handler.write(header_str) for test in tbl_lst: if file_name.split("-")[-2] in test[0]: # cores test[0] = "-".join(test[0].split("-")[:-1]) file_handler.write(",".join([str(item) for item in test]) + "\n") # All tests in txt: tbl_names_txt = ["{0}-1t1c-full.txt".format(table["output-file"]), "{0}-2t2c-full.txt".format(table["output-file"]), "{0}-4t4c-full.txt".format(table["output-file"]) ] for i, txt_name in enumerate(tbl_names_txt): txt_table = None logging.info(" Writing file: '{0}'".format(txt_name)) with open(tbl_names[i], 'rb') as csv_file: csv_content = csv.reader(csv_file, delimiter=',', quotechar='"') for row in csv_content: if txt_table is None: txt_table = prettytable.PrettyTable(row) else: txt_table.add_row(row) txt_table.align["Test case"] = "l" with open(txt_name, "w") as txt_file: txt_file.write(str(txt_table)) def table_performance_trending_dashboard(table, input_data): """Generate the table(s) with algorithm: table_performance_comparison specified in the specification file. :param table: Table to generate. :param input_data: Data to process. :type table: pandas.Series :type input_data: InputData """ logging.info(" Generating the table {0} ...". format(table.get("title", ""))) # Transform the data data = input_data.filter_data(table, continue_on_error=True) # Prepare the header of the tables header = ["Test Case", "Throughput Trend [Mpps]", "Trend Compliance", "Top Anomaly [Mpps]", "Change [%]", "Outliers [Number]" ] header_str = ",".join(header) + "\n" # Prepare data to the table: tbl_dict = dict() for job, builds in table["data"].items(): for build in builds: for tst_name, tst_data in data[job][str(build)].iteritems(): if tbl_dict.get(tst_name, None) is None: name = "{0}-{1}".format(tst_data["parent"].split("-")[0], "-".join(tst_data["name"]. split("-")[1:])) tbl_dict[tst_name] = {"name": name, "data": dict()} try: tbl_dict[tst_name]["data"][str(build)] = \ tst_data["result"]["throughput"] except (TypeError, KeyError): pass # No data in output.xml for this test tbl_lst = list() for tst_name in tbl_dict.keys(): if len(tbl_dict[tst_name]["data"]) > 2: pd_data = pd.Series(tbl_dict[tst_name]["data"]) win_size = pd_data.size \ if pd_data.size < table["window"] else table["window"] # Test name: name = tbl_dict[tst_name]["name"] median = pd_data.rolling(window=win_size, min_periods=2).median() trimmed_data, _ = find_outliers(pd_data, outlier_const=1.5) stdev_t = pd_data.rolling(window=win_size, min_periods=2).std() rel_change_lst = [None, ] classification_lst = [None, ] median_lst = [None, ] sample_lst = [None, ] first = True for build_nr, value in pd_data.iteritems(): if first: first = False continue # Relative changes list: if not isnan(value) \ and not isnan(median[build_nr]) \ and median[build_nr] != 0: rel_change_lst.append(round( relative_change(float(median[build_nr]), float(value)), 2)) else: rel_change_lst.append(None) # Classification list: if isnan(trimmed_data[build_nr]) \ or isnan(median[build_nr]) \ or isnan(stdev_t[build_nr]) \ or isnan(value): classification_lst.append("outlier") elif value < (median[build_nr] - 3 * stdev_t[build_nr]): classification_lst.append("regression") elif value > (median[build_nr] + 3 * stdev_t[build_nr]): classification_lst.append("progression") else: classification_lst.append("normal") sample_lst.append(value) median_lst.append(median[build_nr]) last_idx = len(classification_lst) - 1 first_idx = last_idx - int(table["evaluated-window"]) if first_idx < 0: first_idx = 0 nr_outliers = 0 consecutive_outliers = 0 failure = False for item in classification_lst[first_idx:]: if item == "outlier": nr_outliers += 1 consecutive_outliers += 1 if consecutive_outliers == 3: failure = True else: consecutive_outliers = 0 if failure: classification = "failure" elif "regression" in classification_lst[first_idx:]: classification = "regression" elif "progression" in classification_lst[first_idx:]: classification = "progression" else: classification = "normal" if classification == "normal": index = len(classification_lst) - 1 else: tmp_classification = "outlier" if classification == "failure" \ else classification for idx in range(first_idx, len(classification_lst)): if classification_lst[idx] == tmp_classification: index = idx break for idx in range(index+1, len(classification_lst)): if classification_lst[idx] == tmp_classification: if rel_change_lst[idx] > rel_change_lst[index]: index = idx # if "regression" in classification_lst[first_idx:]: # classification = "regression" # elif "outlier" in classification_lst[first_idx:]: # classification = "outlier" # elif "progression" in classification_lst[first_idx:]: # classification = "progression" # elif "normal" in classification_lst[first_idx:]: # classification = "normal" # else: # classification = None # # nr_outliers = 0 # consecutive_outliers = 0 # failure = False # for item in classification_lst[first_idx:]: # if item == "outlier": # nr_outliers += 1 # consecutive_outliers += 1 # if consecutive_outliers == 3: # failure = True # else: # consecutive_outliers = 0 # # idx = len(classification_lst) - 1 # while idx: # if classification_lst[idx] == classification: # break # idx -= 1 # # if failure: # classification = "failure" # elif classification == "outlier": # classification = "normal" trend = round(float(median_lst[-1]) / 1000000, 2) \ if not isnan(median_lst[-1]) else '' sample = round(float(sample_lst[index]) / 1000000, 2) \ if not isnan(sample_lst[index]) else '' rel_change = rel_change_lst[index] \ if rel_change_lst[index] is not None else '' tbl_lst.append([name, trend, classification, '-' if classification == "normal" else sample, '-' if classification == "normal" else rel_change, nr_outliers]) # Sort the table according to the classification tbl_sorted = list() for classification in ("failure", "regression", "progression", "normal"): tbl_tmp = [item for item in tbl_lst if item[2] == classification] tbl_tmp.sort(key=lambda rel: rel[0]) tbl_sorted.extend(tbl_tmp) file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"]) logging.info(" Writing file: '{0}'".format(file_name)) with open(file_name, "w") as file_handler: file_handler.write(header_str) for test in tbl_sorted: file_handler.write(",".join([str(item) for item in test]) + '\n') txt_file_name = "{0}.txt".format(table["output-file"]) txt_table = None logging.info(" Writing file: '{0}'".format(txt_file_name)) with open(file_name, 'rb') as csv_file: csv_content = csv.reader(csv_file, delimiter=',', quotechar='"') for row in csv_content: if txt_table is None: txt_table = prettytable.PrettyTable(row) else: txt_table.add_row(row) txt_table.align["Test case"] = "l" with open(txt_file_name, "w") as txt_file: txt_file.write(str(txt_table)) def table_performance_trending_dashboard_html(table, input_data): """Generate the table(s) with algorithm: table_performance_trending_dashboard_html specified in the specification file. :param table: Table to generate. :param input_data: Data to process. :type table: pandas.Series :type input_data: InputData """ logging.info(" Generating the table {0} ...". format(table.get("title", ""))) try: with open(table["input-file"], 'rb') as csv_file: csv_content = csv.reader(csv_file, delimiter=',', quotechar='"') csv_lst = [item for item in csv_content] except KeyError: logging.warning("The input file is not defined.") return except csv.Error as err: logging.warning("Not possible to process the file '{0}'.\n{1}". format(table["input-file"], err)) return # Table: dashboard = ET.Element("table", attrib=dict(width="100%", border='0')) # Table header: tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#7eade7")) for idx, item in enumerate(csv_lst[0]): alignment = "left" if idx == 0 else "center" th = ET.SubElement(tr, "th", attrib=dict(align=alignment)) th.text = item # Rows: for r_idx, row in enumerate(csv_lst[1:]): background = "#D4E4F7" if r_idx % 2 else "white" tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background)) # Columns: for c_idx, item in enumerate(row): alignment = "left" if c_idx == 0 else "center" td = ET.SubElement(tr, "td", attrib=dict(align=alignment)) # Name: url = "../trending/" file_name = "" anchor = "#" feature = "" if c_idx == 0: if "memif" in item: file_name = "container_memif.html" elif "vhost" in item: if "l2xcbase" in item or "l2bdbasemaclrn" in item: file_name = "vm_vhost_l2.html" elif "ip4base" in item: file_name = "vm_vhost_ip4.html" elif "ipsec" in item: file_name = "ipsec.html" elif "ethip4lispip" in item or "ethip4vxlan" in item: file_name = "ip4_tunnels.html" elif "ip4base" in item or "ip4scale" in item: file_name = "ip4.html" if "iacl" in item or "snat" in item or "cop" in item: feature = "-features" elif "ip6base" in item or "ip6scale" in item: file_name = "ip6.html" elif "l2xcbase" in item or "l2xcscale" in item \ or "l2bdbasemaclrn" in item or "l2bdscale" in item \ or "l2dbbasemaclrn" in item or "l2dbscale" in item: file_name = "l2.html" if "iacl" in item: feature = "-features" if "x520" in item: anchor += "x520-" elif "x710" in item: anchor += "x710-" elif "xl710" in item: anchor += "xl710-" if "64b" in item: anchor += "64b-" elif "78b" in item: anchor += "78b" elif "imix" in item: anchor += "imix-" elif "9000b" in item: anchor += "9000b-" elif "1518" in item: anchor += "1518b-" if "1t1c" in item: anchor += "1t1c" elif "2t2c" in item: anchor += "2t2c" elif "4t4c" in item: anchor += "4t4c" url = url + file_name + anchor + feature ref = ET.SubElement(td, "a", attrib=dict(href=url)) ref.text = item if c_idx == 2: if item == "regression": td.set("bgcolor", "#eca1a6") elif item == "failure": td.set("bgcolor", "#d6cbd3") elif item == "progression": td.set("bgcolor", "#bdcebe") if c_idx > 0: td.text = item try: with open(table["output-file"], 'w') as html_file: logging.info(" Writing file: '{0}'". format(table["output-file"])) html_file.write(".. raw:: html\n\n\t") html_file.write(ET.tostring(dashboard)) html_file.write("\n\t<p><br><br></p>\n") except KeyError: logging.warning("The output file is not defined.") return