detech-ai-db 0.0.19

detech.ai Database programmatic functions & utils

Database access package for detech.ai

This is detech.ai's package to access Dynamodb & Timestream programatically.

Imports

import detech_query_pkg

###############    DynamoDB Package    ##############################
from detech_query_pkg.dynamodb_pkg import dynamodb_queries as db_queries

from detech_query_pkg.dynamodb_pkg.utils import dynamodb_utils as db_utils

#Start DynamoDB Client
db_utils.create_dynamodb_client(aws_access_key_id=AWS_ACCESS_KEY_ID,
                      aws_secret_access_key=AWS_SECRET_ACCESS_KEY, region_name=REGION_NAME)

###############    Timestream Package    ##############################
from detech_query_pkg.timestream_pkg import ts_queries

from detech_query_pkg.timestream_pkg.utils import ts_utils

from detech_query_pkg.timestream_pkg.models import metrics_model
from detech_query_pkg.timestream_pkg.models import metrics_creator_utils

Initialize Client

def create_dynamodb_client(aws_access_key_id,aws_secret_access_key, region_name)

def create_timestream_session(aws_access_key_id, aws_secret_access_key)

Functions

timestream_pkg (ts_queries.py)

insert_metrics_from_metric_list

def insert_metrics_from_metric_list(metric_list, session)

#Inserts metrics in batch to timestream

#metric_list must have the following fields
metric_list = [
  {'org_id', 'region_name', 'namespace', 'component_id', 'period', 'agent', 'metric_alignment', 'unit', 'description' , 'metric_id', 'metric_name', 'value', 'timestamp'},
  {'org_id', 'region_name', 'namespace', 'component_id', 'period', 'agent', 'metric_alignment', 'unit', 'description' , 'metric_id', 'metric_name', 'value', 'timestamp'},
  ...
]

query_metrics

def query_metrics(sql_query, session)

#Performs an SQL query to timestream and transforms the output to a more desirable format

#Output
query_response = {
  'metric_id': 'qgrdy1bXGeKSmAtW58CD',
  'agent': 'AWS.CloudWatch',
  'component_id': 'AWS/ApplicationELB.app/component',
  'period': '60',
  'unit': 'None',
  'org_id': 'Organization',
  'metric_alignment': 'Sum',
  'namespace': 'AWS/ApplicationELB',
  'description': 'The total number of concurrent TCP connections active from clients to the load balancer and from the load balancer to targets.',
  'region_name': 'eu-west-1',
  'value': '64.0',
  'metric_name': 'ActiveConnectionCount',
  'timestamp': '2020-10-12 14:28:00.000000000'
}

timestream_pkg.utils (ts_utils.py)

prepare_metric_records

def prepare_metric_records(measure_name, measure_value, timestamp, dimensions)

#Creates the metrics records necessary to use the write_to_timestream function

#The dimensions that need to be passed must be in the following format
dimensions = [
  {'Name':'org_id', 'Value': str(metric['org_id'])},
  {'Name':'region_name', 'Value':str(metric['region_name'])},
  {'Name':'namespace', 'Value':str(metric['namespace'])},
  {'Name':'component_id', 'Value':str(metric['component_id'])},
  {'Name':'period', 'Value': str(metric['period'])},
  {'Name':'agent', 'Value':str(metric['agent'])},
  {'Name':'metric_alignment', 'Value':str(metric['metric_alignment'])},
  {'Name':'unit', 'Value':str(metric['unit'])},
  {'Name': 'description', 'Value': str(metric['description'])},
  {'Name': 'metric_id', 'Value':str(metric['metric_id'])}
]

write_to_timestream

def write_to_timestream(records, database_name, table_name, ts_session)

#Inserts metrics to timestream after they are in the correct format

query_from_timestream

def query_from_timestream(sql_query, database_name, table_name,ts_session)

#Queries metrics from timestream with a given sql_query

timestream_pkg.models (metric_creator_utils.py & metrics_model.py)

build_metric_model

#from metric_creator_utils.py
def build_metric_model(metric_id, metric_name, org_id, component_id,
  namespace, metric_alignment, agent, dimensions, region_name=None,
  is_default=False, description=None, period=60,unit=None, samples=[])

#Queries metrics from timestream with a given sql_query

MetricModel

#from metrics_model.py
class MetricModel(object):
  def __init__(self, metric_id,metric_name, org_id, component_id, namespace,
    metric_alignment, region_name, agent, dimensions = {},
    is_default=False, description=None, period=60,unit=None, samples=[])

  def to_dict(self)

#Queries metrics from timestream with a given sql_query

dynamodb_pkg

insert_alert

def insert_alert(alert_id, metric_id, org_id, app_id, team_id, assigned_to, start_time, end_time, alert_description, is_acknowledged, anomalies_dict, related_prev_anomalies,  service_graph, significance_score, dynamodb)

#Example
insert_alert(alert_id = "256828", metric_id = 123, org_id = 'org_id', app_id = 'app_id', team_id = 'team_id', assigned_to = 'Jorge', \
start_time = '2020-09-03 12:00:00', end_time = '2020-09-03 12:20:00', alert_description = 'Spike in costs',\
is_acknowledged = 'True', anomalies_dict = {}, related_prev_anomalies = {},
service_graph = {}, significance_score = '34.3')

get_alert_item_by_key

def get_alert_item_by_key(anom_id, dynamodb)

update_alert_with_related_anomalies

def update_alert_with_related_anomalies(alert_id,start_time, corr_anoms_dict, related_prev_anomalies, dynamodb)

terminate_alert

def terminate_alert(alert_id,start_time, end_timestamp, dynamodb)

create_metric

def create_metric(metric_id, date_bucket, metric_name, provider, namespace,
agent, org_id, app_id, alignment, groupby, dimensions, data_points_list, dynamodb)

#Example
create_metric(
  metric_id = "test1", date_bucket = "2020-10-02", metric_name = "error_rate",
  provider = "aws", namespace = "dynamodb", agent = "CloudWatch", org_id = "test",
  app_id = "app1", alignment = "Sum",
  dimensions = [{"Name": "TableName", "Value": "alerts.config"}],
  last = 1535530432, data_points_list = [
    { 'val': 55, 'time' : 1535530430},
    { 'val': 56, 'time': 1535530432}], dynamodb=dynamodb
)

batch_insert_metric_objects

def batch_insert_metric_details_objects(list_of_metric_objects, dynamodb)
#Inserts list of metrics objects in batch into Dynamodb

batch_insert_metric_objects

def batch_insert_metric_details_objects(list_of_metric_objects, dynamodb)
#Inserts list of metrics objects in batch into Dynamodb

batch_insert_metric_objects

def batch_insert_component_info_objects(list_of_component_objects, dynamodb)
#Inserts list of component objects in batch into Dynamodb

get_metric_details

def get_metric_details(metric_id, dynamodb)
#Fetches all the details for a specific metric_id

get_metric_item_by_key

def get_metric_item_by_key(metric_id, curr_date, dynamodb)

scan_metrics_by_encrypted_id

def scan_metrics_by_encrypted_id(anom_alarm_id, dynamodb)

query_alerts_configs_by_key

def query_alerts_configs_by_key(metric_id, dynamodb)

insert_alert_config

def insert_alert_config(metric_id, alert_title, severity, alert_type, alert_direction, description, duration, duration_unit, rule_dict, recipients_list, owner_dict, dynamodb)

#Example
insert_alert_config(
  metric_id = "metric1245", alert_title = "Anomaly by Cluster", severity = "critical",
  alert_type = "anomaly", alert_direction = "spikes/drops", description = "Relevant to Play Store billing user journey",
  duration= 12, duration_unit = "hours", rule_dict = {}, recipients_list = [{
    "channel" : "webhook",
    "contact" : "[e-mail]"
    },{
      "channel" : "slack",
      "contact" : "[e-mail]"
    }
  ],
  owner_dict = {
    "user_id" : "user12341",
    "user_name" : "João Tótó",
  }
)

query_most_recent_metric_fetching_log

def query_most_recent_metric_fetching_log(component_id, dynamodb)
#Fetches the log with the highest timestamp, from all the logs between start & end ts

dynamodb_pkg.utils

put_item

def put_item(item_dict, table_name, dynamodb)
#Inserts json item into DynamoDB table

#Example
item_dict = {
  "attr" : "value",
  "attr2" : "value2"
}
table_name = "alerts"

batch_insert

def batch_insert(list_of_item_dicts, table_name, dynamodb)
#Inserts a list of item_dicts in batch to dynamodb

get_item

def get_item(key_dict, table_name, dynamodb)
#Retrieves item from DynamoDB table

#Example
key_dict = {
  "prim_key" = "value",
  "sort_key" = "value"
}

get_item_and_retrieve_specific_attributes

def get_item_and_retrieve_specific_attributes(key_dict, attr_list, table_name, dynamodb)
#Retrieves item from DynamoDB table and retrieve specific attributes

#Example
key_dict = {
  "prim_key" :"value",
  "sort_key" : "value"
}
attr_list = ['attr1', 'attr2']

update_item

def update_item(key_dict, update_expression, expression_attr_values, table_name, dynamodb)
#Retrieves item from DynamoDB table

#Example
key_dict = {
  "prim_key" = "value",
  "sort_key" = "value"
}
update_expression = "set service_graph=:i, metric_list=:l, significance_score=:s"
expression_attr_values = {
  ':i': {'s1':['s2', 's3']},
  ':l': ['124','123'],
  ':s': Decimal(35.5)
}
#example to append to list
UpdateExpression="SET some_attr = list_append(if_not_exists(some_attr, :empty_list), :i)",
ExpressionAttributeValues={
  ':i': [some_value],
  "empty_list" : []
}

update_item_conditionally

def update_item_conditionally(key_dict, condition_expression, update_expression, expression_attr_values, table_name, dynamodb)
#Retrieves item from DynamoDB table

#Example
key_dict = {
  "prim_key" = "value",
  "sort_key" = "value"
}
update_expression = "set service_graph=:i, metric_list=:l, significance_score=:s"
expression_attr_values = {
  ':i': {'s1':['s2', 's3']},
  ':l': ['124','123'],
  ':s': Decimal(35.5)
}
condition_expression = "significance_score <= :val"

delete_item_conditionally

def delete_item_conditionally(key_dict, condition_expression, expression_attr_values, table_name, dynamodb)

#Example
condition_expression = "significance_score <= :val"
expression_attr_values = {
  ":val": Decimal(50)
}
key_dict = {
  'org_id': 'Aptoide',
  'start_time': '2020-09-03 12:00:00'
}
'''

query_by_key

def query_by_key(key_condition, table_name, dynamodb)
#Queries from DynamoDB table by key condition

#Example
key_condition = Key('org_id').eq('Aptoide')

query_and_project_by_key_condition

def query_and_project_by_key_condition(projection_expr, expr_attr_names, key_condition, table_name, dynamodb)
#Queries from DynamoDB table by key condition and only returns some attrs

#Example
key_condition = Key('year').eq(year) & Key('title').between(title_range[0], title_range[1])
projection_expr = "#yr, title, info.genres, info.actors[0]"
expr_attr_names = {"#yr": "year"}

scan_table

def scan_table(scan_kwargs, table_name, dynamodb)
#Scans entire table looking for items that match the filter expression

#Example
scan_kwargs = {
  'FilterExpression': Key('year').between(*year_range),
  'ProjectionExpression': "#yr, title, info.rating",
  'ExpressionAttributeNames': {"#yr": "year"}
}

query_by_key_min_max

def query_by_key_min_max(key_condition, table_name, is_min, dynamodb)
#Queries from DynamoDB table by key condition

#Example
key_condition = Key('part_id').eq(partId) & Key('range_key').between(start, end)
#or
key_condition = Key('part_id').eq(partId)

get_all_items_in_table

def get_all_items_in_table(table_name, dynamodb)

increment_atomic_counter

def increment_atomic_counter(counter_type, dynamodb)
#Increments a counter and makes sure it is done atomically
#Available counter types:
#org_id
#component_id
#metric_id