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Krystian Piwowarczyk 2024-05-28 14:23:37 +00:00
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.gitignore vendored
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.c9
przetwarzanie_danych_w_chmurze/labs/terraform/.terraform
labs/terraform/.terraform
labs/terraform/terraform.tfstate
labs/terraform/terraform.tfstate.backup

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README.md
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___ ______ ____ _ _ ___
/ \ \ / / ___| / ___| | ___ _ _ __| |/ _ \
/ _ \ \ /\ / /\___ \ | | | |/ _ \| | | |/ _` | (_) |
/ ___ \ V V / ___) | | |___| | (_) | |_| | (_| |\__, |
/_/ \_\_/\_/ |____/ \____|_|\___/ \__,_|\__,_| /_/
-----------------------------------------------------------------
Hi there! Welcome to AWS Cloud9!
To get started, create some files, play with the terminal,
or visit https://docs.aws.amazon.com/console/cloud9/ for our documentation.
Happy coding!
Repozytorium przygotowane w ramach zajęć Przetwarzanie danych w chmurze, autor Krystian Piwowarczyk s1201686 krypiw@st.amu.edu.pl .

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resource "aws_lambda_layer_version" "aws_wrangler" {
filename = "../lambda/awswrangler-layer-2.7.0-py3.8.zip"
#filename = "../lambda/awswrangler-layer-2.7.0-py3.8.zip"
layer_name = "aws_wrangler_${var.account_number}_${var.student_initials}_${var.student_index_no}"
source_code_hash = "${filebase64sha256("../lambda/awswrangler-layer-2.7.0-py3.8.zip")}"
#source_code_hash = "${filebase64sha256("../lambda/awswrangler-layer-2.7.0-py3.8.zip")}"
compatible_runtimes = ["python3.8"]
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1 Format danych AVRO\n",
"\n",
"Ćwiczenie ma na celu zademonstrowanie schematów danych AVRO, typów złożonych (mapy, listy, struktury zagnieżdżone) oraz wstęp do Glue / Athena\n",
"\n",
"## Przebieg ćwiczenia\n",
"* skonfiguruj środowisko uruchomieniowe Python (sugerowana Anaconda z Python 3)\n",
"* zainstaluj wszystkie wymagane biblioteki\n",
"\n",
"<code>\n",
"% conda create -n myenv python=3.8\n",
"% conda activate uam-datalake\n",
"% pip install -r ./datalake-uam/jupyter/requirements.txt\n",
"</code>\n",
"\n",
"* zaloguj się do konsoli AWS i stwórz Bucket testowy oraz bazę dancyh w Glue. Uzupełnij poniższy skrypt o te dane \n",
"* wygeneruj dane testowe w wybranym schemacie AVRO\n",
"* zapisz dane do plików na S3 w folderach s3:/<twoj-bukcet-name>/EventName/namespace=xxx/year=YYYY/month=MM/day=DD/version=VVV\n",
"* zarejestruj tabele w Glue z wykorzystaniem BOTO3 / crawler (poprzez konsole AWS GUI - przeglądarkę)\n",
"* skonfiguruj domyślną WorkGroup w Athena (PRIMARY) - konieczne wskazanie miejsce docelowego dla danych z zapytań (S3 location Athena) https://docs.aws.amazon.com/athena/latest/ug/getting-started.html\n",
"* sprawdź definicję tabeli i upewnij się że są zarejestrowane partycje (użyj polecenia MSCK REPAIR w Athena (LOAD PARTITIONS)\n",
"* sprawdź ile danych jest w tabeli (select count(*) from table) - Data Scanned in bytes\n",
"* odpytaj tabele z wykorzystaniem predykatu day=1 (partition elimination) - zweryfikuj ilość danych przeskanowanych (do porównania z ćwiczeniem 2 - parquet)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import boto3\n",
"\n",
"REGION = \"us-east-1\"\n",
"\n",
"\n",
"session_kwargs = {\n",
"\n",
" \"aws_access_key_id\":\"\",\n",
" \"aws_secret_access_key\":\"\",\n",
" \"aws_session_token\":\"\",\n",
" \"region_name\": REGION\n",
"}\n",
" \n",
"session = boto3.Session(**session_kwargs)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"\n",
"from faker import Faker\n",
"from botocore.exceptions import ClientError\n",
"from avro.datafile import DataFileReader, DataFileWriter\n",
"from avro.io import DatumReader, DatumWriter\n",
"import time \n",
"import io\n",
"import datetime\n",
"from avro.schema import Parse\n",
"\n",
"\n",
"fake = Faker()\n",
"fake.seed_instance(4321)\n",
" \n",
"S3_BUCKET = \"datalake-dev-920628590621-us-east-1\"\n",
"\n",
"TEST_DB = 'datalake_dev_jk'\n",
"TEST_TABLE_NAME = 'avro_uam_test'\n",
"EVENT_NAME = \"UamTestEvent\"\n",
"\n",
"\n",
"s3_client = session.client(\"s3\")\n",
"glue_client = session.client(\"glue\")\n",
"\n",
"\n",
"def tear_down_test_db(database=TEST_DB):\n",
" db_names = [x[\"Name\"] for x in glue_client.get_databases()[\"DatabaseList\"] ]\n",
" if database in db_names:\n",
" glue_client.delete_database(Name=database)\n",
" print(\"{} deleted\".format(database))\n",
"\n",
" response_create_db = glue_client.create_database(DatabaseInput={'Name': database }) \n",
" print(\"%s db recreated\" % database)\n",
"\n",
"def tear_down_test_table(database=TEST_DB, table_name=TEST_TABLE_NAME):\n",
" tbl_list = [x[\"Name\"] for x in glue_client.get_tables(DatabaseName=database)[\"TableList\"]]\n",
" if table_name in tbl_list:\n",
" glue_client.delete_table(DatabaseName=database,Name=table_name)\n",
" print(\"test table {} deleted\".format(table_name))\n",
" else:\n",
" print(\"tbl %s not found\" % table_name)\n",
" \n",
"def tear_down_s3(bucket=S3_BUCKET,prefix=EVENT_NAME):\n",
" s3 = boto3.resource('s3',**session_kwargs)\n",
" bucket = s3.Bucket(bucket)\n",
" bucket.objects.filter(Prefix=prefix).delete()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. AVRO schema"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"UamTestEvent com.uam.datalake.v1 1.0.2\n"
]
},
{
"data": {
"text/plain": [
"{'mox-meta': {'version': '1.0.2', 'type': 'ENTITY_SNAPSHOT'},\n",
" 'namespace': 'com.uam.datalake.v1',\n",
" 'type': 'record',\n",
" 'name': 'UamTestEvent',\n",
" 'fields': [{'name': 'customerId',\n",
" 'type': {'type': 'string', 'avro.java.string': 'String'}},\n",
" {'name': 'isActive',\n",
" 'type': 'boolean',\n",
" 'doc': 'a boolean flag if the Customer is active'},\n",
" {'name': 'age', 'type': 'int'},\n",
" {'name': 'balance', 'type': 'float'},\n",
" {'name': 'accountBalance_logical_dec',\n",
" 'type': {'type': 'bytes',\n",
" 'logicalType': 'decimal',\n",
" 'precision': 20,\n",
" 'scale': 4}},\n",
" {'name': 'array_of_strings',\n",
" 'type': ['null',\n",
" {'type': 'array',\n",
" 'items': {'type': 'string', 'avro.java.string': 'String'}}],\n",
" 'default': None},\n",
" {'name': 'paymentDetails',\n",
" 'type': ['null',\n",
" {'type': 'record',\n",
" 'name': 'PaymentDetails',\n",
" 'fields': [{'name': 'counterPartyName',\n",
" 'type': ['null', {'type': 'string', 'avro.java.string': 'String'}],\n",
" 'default': None},\n",
" {'name': 'groupingId',\n",
" 'type': ['null', {'type': 'string', 'avro.java.string': 'String'}],\n",
" 'default': None},\n",
" {'name': 'payeeId',\n",
" 'type': ['null', {'type': 'string', 'avro.java.string': 'String'}],\n",
" 'default': None},\n",
" {'name': 'message',\n",
" 'type': ['null', {'type': 'string', 'avro.java.string': 'String'}],\n",
" 'default': None},\n",
" {'name': 'type',\n",
" 'type': {'type': 'enum',\n",
" 'name': 'PaymentType',\n",
" 'symbols': ['UNKNOWN', 'ONE', 'TWO']}},\n",
" {'name': 'otherAccountId',\n",
" 'type': ['null', {'type': 'string', 'avro.java.string': 'String'}],\n",
" 'default': None}]}],\n",
" 'default': None},\n",
" {'name': 'parameters',\n",
" 'type': ['null',\n",
" {'type': 'map',\n",
" 'avro.java.string': 'String',\n",
" 'values': {'type': 'string', 'avro.java.string': 'String'}}],\n",
" 'default': None}]}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# test Avro Schema with all important cases\n",
"\n",
"1\n",
"\n",
"schema_string = \"\"\"\n",
"{\n",
" \"mox-meta\":{\n",
" \"version\":\"1.0.2\",\n",
" \"type\":\"ENTITY_SNAPSHOT\"\n",
" },\n",
" \"namespace\":\"com.uam.datalake.v1\",\n",
" \"type\":\"record\",\n",
" \"name\":\"\",\n",
" \"fields\":[\n",
" {\n",
" \"name\":\"customerId\",\n",
" \"type\":{\n",
" \"type\":\"string\",\n",
" \"avro.java.string\":\"String\"\n",
" }\n",
" },\n",
" {\n",
" \"name\":\"isActive\",\n",
" \"type\":\"boolean\",\n",
" \"doc\":\"a boolean flag if the Customer is active\"\n",
" },\n",
" {\n",
" \"name\":\"age\",\n",
" \"type\":\"int\"\n",
" },\n",
" {\n",
" \"name\":\"balance\",\n",
" \"type\":\"float\"\n",
" },\n",
" {\n",
" \"name\":\"accountBalance_logical_dec\",\n",
" \"type\":{\n",
" \"type\":\"bytes\",\n",
" \"logicalType\":\"decimal\",\n",
" \"precision\":20,\n",
" \"scale\":4\n",
" }\n",
" },\n",
" {\n",
" \"name\":\"array_of_strings\",\n",
" \"type\":[\n",
" \"null\",\n",
" {\n",
" \"type\":\"array\",\n",
" \"items\":{\n",
" \"type\":\"string\",\n",
" \"avro.java.string\":\"String\"\n",
" }\n",
" }\n",
" ],\n",
" \"default\":null\n",
" },\n",
" {\n",
" \"name\":\"paymentDetails\",\n",
" \"type\":[\n",
" \"null\",\n",
" {\n",
" \"type\":\"record\",\n",
" \"name\":\"PaymentDetails\",\n",
" \"fields\":[\n",
" {\n",
" \"name\":\"counterPartyName\",\n",
" \"type\":[\n",
" \"null\",\n",
" {\n",
" \"type\":\"string\",\n",
" \"avro.java.string\":\"String\"\n",
" }\n",
" ],\n",
" \"default\":null\n",
" },\n",
" {\n",
" \"name\":\"groupingId\",\n",
" \"type\":[\n",
" \"null\",\n",
" {\n",
" \"type\":\"string\",\n",
" \"avro.java.string\":\"String\"\n",
" }\n",
" ],\n",
" \"default\":null\n",
" },\n",
" {\n",
" \"name\":\"payeeId\",\n",
" \"type\":[\n",
" \"null\",\n",
" {\n",
" \"type\":\"string\",\n",
" \"avro.java.string\":\"String\"\n",
" }\n",
" ],\n",
" \"default\":null\n",
" },\n",
" {\n",
" \"name\":\"message\",\n",
" \"type\":[\n",
" \"null\",\n",
" {\n",
" \"type\":\"string\",\n",
" \"avro.java.string\":\"String\"\n",
" }\n",
" ],\n",
" \"default\":null\n",
" },\n",
" {\n",
" \"name\":\"type\",\n",
" \"type\":{\n",
" \"type\":\"enum\",\n",
" \"name\":\"PaymentType\",\n",
" \"symbols\":[\n",
" \"UNKNOWN\",\n",
" \"ONE\",\n",
" \"TWO\" \n",
" ]\n",
" }\n",
" },\n",
" {\n",
" \"name\":\"otherAccountId\",\n",
" \"type\":[\n",
" \"null\",\n",
" {\n",
" \"type\":\"string\",\n",
" \"avro.java.string\":\"String\"\n",
" }\n",
" ],\n",
" \"default\":null\n",
" }\n",
" ]\n",
" }\n",
" ],\n",
" \"default\":null\n",
" },\n",
" {\n",
" \"name\":\"parameters\",\n",
" \"type\":[\n",
" \"null\",\n",
" {\n",
" \"type\":\"map\",\n",
" \"avro.java.string\":\"String\",\n",
" \"values\":{\n",
" \"type\":\"string\",\n",
" \"avro.java.string\":\"String\"\n",
" }\n",
" }\n",
" ],\n",
" \"default\":null\n",
" }\n",
" ]\n",
"}\n",
"\"\"\"\n",
"\n",
"schema = json.loads(schema_string)\n",
"schema[\"name\"] = EVENT_NAME\n",
"\n",
"RECORD_NAME = schema[\"name\"]\n",
"NAMESPACE = schema[\"namespace\"]\n",
"VERSION = schema[\"mox-meta\"][\"version\"]\n",
"\n",
"print('%s %s %s' %(RECORD_NAME,NAMESPACE,VERSION))\n",
"schema"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Generating test data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# generate some avro data (buffer file) based on the above schema\n",
"\n",
"avro_schema = Parse(json.dumps(schema))\n",
"buf = io.BytesIO()\n",
"writer = DataFileWriter(buf, DatumWriter(), avro_schema)\n",
"\n",
"for x in range(0, 10000):\n",
"\n",
"\n",
" customer_id = fake.uuid4()\n",
" amount = fake.pydecimal(left_digits=8, right_digits=4)\n",
" amount_int = int(str(amount).replace('.', ''))\n",
"\n",
" strings_arrray = [fake.first_name() for x in range(0, fake.random.randint(1, 5))]\n",
"\n",
" paymentDetails = {'counterPartyName': customer_id,\n",
" 'groupingId': str(fake.uuid4()), 'payeeId': None, 'message': None,\n",
" 'type': 'ONE', 'otherAccountId': str(fake.uuid4())}\n",
" \n",
" randint = fake.random.randint(20, 70)\n",
" \n",
"\n",
" array_of_structs = [{\"field1\": \"one\"}, {\"field1\": \"two\"}]\n",
" customer = {\n",
" \"customerId\": customer_id,\n",
" \"isActive\": fake.random.choice([True, False]),\n",
" \"age\": randint,\n",
" \"balance\": fake.random.random() * 123,\n",
" \"accountBalance_logical_dec\": amount_int.to_bytes(amount_int.bit_length() // 8 + 1, byteorder='big',\n",
" signed=True),\n",
" \"array_of_strings\": strings_arrray,\n",
" \"paymentDetails\": paymentDetails,\n",
"\n",
" \"parameters\": {\"key1\": \"value1\", \"key2\": \"value2\"}\n",
"\n",
" }\n",
" writer.append(customer)\n",
"\n",
"\n",
"writer.flush()\n",
"raw_bytes = buf.getvalue()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"uploaded UamTestEvent/namespace=com.uam.datalake.v1/year=2020/month=2/day=1/version=1.0.2/CustData_d6f44c22-5c87-4e14-956b-ad7d985226d0.avro\n",
"uploaded UamTestEvent/namespace=com.uam.datalake.v1/year=2020/month=2/day=2/version=1.0.2/CustData_cf0edf74-e76e-458f-a1d2-e092275a719c.avro\n",
"uploaded UamTestEvent/namespace=com.uam.datalake.v1/year=2020/month=2/day=3/version=1.0.2/CustData_f9ed4ad7-ed1c-4431-b9ab-317387cdb5af.avro\n",
"uploaded UamTestEvent/namespace=com.uam.datalake.v1/year=2020/month=2/day=4/version=1.0.2/CustData_d89b1c7f-66e3-4522-a603-1630abbf24fa.avro\n",
"uploaded UamTestEvent/namespace=com.uam.datalake.v1/year=2020/month=2/day=5/version=1.0.2/CustData_92483d0c-5254-4825-9bbb-59545fc6e4dd.avro\n",
"uploaded UamTestEvent/namespace=com.uam.datalake.v1/year=2020/month=2/day=6/version=1.0.2/CustData_3473649b-97c5-4597-965b-672a11cdad73.avro\n",
"uploaded UamTestEvent/namespace=com.uam.datalake.v1/year=2020/month=2/day=7/version=1.0.2/CustData_ecc999f8-eda7-4782-b844-17809980f34c.avro\n",
"uploaded UamTestEvent/namespace=com.uam.datalake.v1/year=2020/month=2/day=8/version=1.0.2/CustData_b95fd45c-8f0d-4612-8f8b-131437895013.avro\n",
"uploaded UamTestEvent/namespace=com.uam.datalake.v1/year=2020/month=2/day=9/version=1.0.2/CustData_5a5ef6ba-1576-4450-82a0-3f6f9a10a7c8.avro\n",
"uploaded UamTestEvent/namespace=com.uam.datalake.v1/year=2020/month=2/day=10/version=1.0.2/CustData_4e5dc9b8-6eb7-4fa4-93e0-61faf874b698.avro\n"
]
}
],
"source": [
"tear_down_s3()\n",
"\n",
"for i in range(1,11):\n",
"\n",
" target_key_name = '{record_name}/namespace={ns}/year=2020/month=2/day={day}/version={ver}/CustData_{rand}.avro'.format(\n",
" record_name=RECORD_NAME,ns=NAMESPACE, day=i,ver=VERSION, rand=fake.uuid4())\n",
" try:\n",
" response = s3_client.put_object(Body=raw_bytes, Bucket=S3_BUCKET, Key=target_key_name)\n",
" print(\"uploaded %s\" % target_key_name)\n",
" except ClientError as e:\n",
" logging.error(e)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Avro reading"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"UamTestEvent/namespace=com.uam.datalake.v1/year=2020/month=2/day=10/version=1.0.2/CustData_4e5dc9b8-6eb7-4fa4-93e0-61faf874b698.avro\n",
"{'customerId': 'cc733c92-6853-45f6-8e49-bec741188ebb', 'isActive': True, 'age': 58, 'balance': 49.34309768676758, 'accountBalance_logical_dec': b'7L\\xbc\\xff\\xf3', 'array_of_strings': ['Rebecca'], 'paymentDetails': {'counterPartyName': 'cc733c92-6853-45f6-8e49-bec741188ebb', 'groupingId': '9626bf79-2f97-4c0c-9aae-de080adab7df', 'payeeId': None, 'message': None, 'type': 'ONE', 'otherAccountId': '69261bc2-4a71-4de7-bc8b-1beb0d9320ac'}, 'parameters': {'key1': 'value1', 'key2': 'value2'}}\n"
]
}
],
"source": [
"print(target_key_name)\n",
"\n",
"obj = s3_client.get_object(Bucket=S3_BUCKET, Key=target_key_name)\n",
"record_raw = obj['Body'].read()\n",
"\n",
"\n",
"reader = DataFileReader(io.BytesIO(record_raw), DatumReader())\n",
"for line in reader:\n",
" print(line)\n",
" break\n",
"\n",
"avro_schema = reader.meta[\"avro.schema\"]\n",
"reader.close()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"b'{\"type\": \"record\", \"mox-meta\": {\"version\": \"1.0.2\", \"type\": \"ENTITY_SNAPSHOT\"}, \"name\": \"UamTestEvent\", \"namespace\": \"com.uam.datalake.v1\", \"fields\": [{\"type\": {\"type\": \"string\", \"avro.java.string\": \"String\"}, \"name\": \"customerId\"}, {\"type\": \"boolean\", \"name\": \"isActive\", \"doc\": \"a boolean flag if the Customer is active\"}, {\"type\": \"int\", \"name\": \"age\"}, {\"type\": \"float\", \"name\": \"balance\"}, {\"type\": {\"type\": \"bytes\", \"logicalType\": \"decimal\", \"precision\": 20, \"scale\": 4}, \"name\": \"accountBalance_logical_dec\"}, {\"type\": [\"null\", {\"type\": \"array\", \"items\": {\"type\": \"string\", \"avro.java.string\": \"String\"}}], \"name\": \"array_of_strings\", \"default\": null}, {\"type\": [\"null\", {\"type\": \"record\", \"name\": \"PaymentDetails\", \"namespace\": \"com.uam.datalake.v1\", \"fields\": [{\"type\": [\"null\", {\"type\": \"string\", \"avro.java.string\": \"String\"}], \"name\": \"counterPartyName\", \"default\": null}, {\"type\": [\"null\", {\"type\": \"string\", \"avro.java.string\": \"String\"}], \"name\": \"groupingId\", \"default\": null}, {\"type\": [\"null\", {\"type\": \"string\", \"avro.java.string\": \"String\"}], \"name\": \"payeeId\", \"default\": null}, {\"type\": [\"null\", {\"type\": \"string\", \"avro.java.string\": \"String\"}], \"name\": \"message\", \"default\": null}, {\"type\": {\"type\": \"enum\", \"name\": \"PaymentType\", \"namespace\": \"com.uam.datalake.v1\", \"symbols\": [\"UNKNOWN\", \"ONE\", \"TWO\"]}, \"name\": \"type\"}, {\"type\": [\"null\", {\"type\": \"string\", \"avro.java.string\": \"String\"}], \"name\": \"otherAccountId\", \"default\": null}]}], \"name\": \"paymentDetails\", \"default\": null}, {\"type\": [\"null\", {\"type\": \"map\", \"avro.java.string\": \"String\", \"values\": {\"type\": \"string\", \"avro.java.string\": \"String\"}}], \"name\": \"parameters\", \"default\": null}]}'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"avro_schema"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tbl avro_uam_test not found\n"
]
},
{
"data": {
"text/plain": [
"{'ResponseMetadata': {'RequestId': '1d5a05a1-0253-436c-a85a-d21f334d51ae',\n",
" 'HTTPStatusCode': 200,\n",
" 'HTTPHeaders': {'date': 'Sat, 24 Apr 2021 11:20:04 GMT',\n",
" 'content-type': 'application/x-amz-json-1.1',\n",
" 'content-length': '2',\n",
" 'connection': 'keep-alive',\n",
" 'x-amzn-requestid': '1d5a05a1-0253-436c-a85a-d21f334d51ae'},\n",
" 'RetryAttempts': 0}}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#register glue table with avro SCHEMA\n",
"tear_down_test_table() # create or replace\n",
"\n",
"glue_client.create_table(\n",
" DatabaseName=TEST_DB,\n",
" TableInput={\n",
" \"Name\" : TEST_TABLE_NAME,\n",
" 'Owner': 'owner',\n",
" 'StorageDescriptor': {\n",
" 'Columns': [\n",
" {'Name': 'customerId', 'Type': 'string'},\n",
" {'Name': 'isActive', 'Type': 'boolean'},\n",
" {'Name': 'age', 'Type': 'int'}, \n",
" {'Name': 'balance', 'Type': 'float'},\n",
" {'Name': 'accountBalance_logical_dec', 'Type': 'decimal(20,4)'},\n",
" {'Name': 'array_of_strings', 'Type': 'array<string>'},\n",
" {'Name': 'paymentdetails',\n",
" 'Type': 'struct<counterpartyname:string,groupingid:string,payeeid:string,message:string,type:string,otheraccountid:string>'},\n",
" {'Name': 'parameters', 'Type': 'map<string,string>'}\n",
" ],\n",
" 'Location': 's3://{}/{}/namespace={}/'.format(S3_BUCKET,RECORD_NAME,NAMESPACE),\n",
" 'InputFormat': 'org.apache.hadoop.hive.ql.io.avro.AvroContainerInputFormat',\n",
" 'OutputFormat': 'org.apache.hadoop.hive.ql.io.avro.AvroContainerOutputFormat',\n",
" 'Compressed': False,\n",
" 'NumberOfBuckets': -1,\n",
" 'SerdeInfo': {\n",
" 'SerializationLibrary': 'org.apache.hadoop.hive.serde2.avro.AvroSerDe',\n",
" 'Parameters': \n",
" {\n",
" 'avro.schema.literal': json.dumps(schema),\n",
" 'serialization.format': '1'\n",
" }\n",
" },\n",
" 'BucketColumns': [],\n",
" 'SortColumns': [],\n",
" },\n",
" 'PartitionKeys': [\n",
" {'Name': 'year','Type': 'int'},\n",
" {'Name': 'month','Type': 'int'},\n",
" {'Name': 'day','Type': 'int'},\n",
" {'Name': 'version','Type': 'string'}\n",
" ],\n",
" 'TableType': 'EXTERNAL_TABLE', \n",
" 'Parameters': {\n",
" \n",
" 'avro.schema.literal': json.dumps(schema),\n",
" 'classification': 'avro',\n",
" 'compressionType': 'none',\n",
" \n",
" }\n",
" }\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### now you can find the table in Glue Data Catalogue and query with Athena (remember about partitions)\n",
"\n",
"```\n",
"SELECT * , paymentdetails.groupingid , \"$path\"\n",
"FROM \"avro_uam_test\" as a \n",
"CROSS JOIN UNNEST(array_of_strings) as t(names)\n",
"where customerid = '0d6f913f-9364-4898-875e-d07311d1e300' and day = 1\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

606
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41
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@ -1,41 +0,0 @@
### Runbook kafka stack + kafka connect
1. uruchom stack
```
docker-compose -f docker-compose.yml up
```
2. Sprawdz jakie sa topiki na Kafce
```
./kafka-topics.sh --bootstrap-server localhost:9092 --list
```
3. Stworz nowy topik : test-topic
```
./kafka-topics.sh --bootstrap-server localhost:9092 --topic json.test.topic --create --partitions 3 --replication-factor
```
4. Połącz się producerem do Kafki na topik test-topic
```
./kafka-console-producer.sh --broker-list localhost:9092 --topic json.test.topic
```
5. Połącz się z Kafką na json.test-topic
```
./kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic json.test.topic --from-beginning --property print.timestamp=true --from-beginning --property print.key=true
```
6. Deploy connectors
Najpierw simple connector - wyślij na http://localhost:8083/connectors/ (POST method) definicję UAM_3_sink_connector_simple.json np. z Postmana
Teraz napisz kilka wiadomości i poczekaj aż zrzuci do Minio
7. `docker-compose logs minio -f`
`docker-compose ps`

39
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@ -1,39 +0,0 @@
{
"name": "sink-s3-bytes",
"config": {
"timestamp.extractor": "Record",
"locale": "US",
"timezone": "UTC",
"connector.class": "io.confluent.connect.s3.S3SinkConnector",
"tasks.max": "3",
"s3.region": "ap-southeast-1",
"s3.bucket.name": "dev-data-raw",
"s3.acl.canned": "bucket-owner-full-control",
"s3.part.size": "5242880",
"flush.size": "10",
"rotate.interval.ms": "3600000",
"rotate.schedule.interval.ms": "3000",
"storage.class": "io.confluent.connect.s3.storage.S3Storage",
"format.class": "io.confluent.connect.s3.format.json.JsonFormat",
"value.converter": "org.apache.kafka.connect.converters.ByteArrayConverter",
"value.converter.schemas.enable": "false",
"key.converter": "org.apache.kafka.connect.converters.ByteArrayConverter",
"key.converter.schemas.enable": "false",
"partitioner.class": "io.confluent.connect.storage.partitioner.TimeBasedPartitioner",
"path.format": "'year'=YYYY/'month'=M/'day'=d/'hour'=H",
"partition.duration.ms": "3600000",
"schema.generator.class": "io.confluent.connect.storage.hive.schema.DefaultSchemaGenerator",
"schema.compatibility": "NONE",
"name": "sink-s3-bytes",
"topics.regex": ".*",
"topics.dir": "sink-s3-bytes",
"transforms": "MakeMap, InsertMetadata",
"transforms.MakeMap.type": "org.apache.kafka.connect.transforms.HoistField$Value",
"transforms.MakeMap.field": "msg_payload",
"transforms.InsertMetadata.type": "org.apache.kafka.connect.transforms.InsertField$Value",
"transforms.InsertMetadata.partition.field": "msg_partition",
"transforms.InsertMetadata.offset.field": "msg_offset",
"transforms.InsertMetadata.timestamp.field": "msg_ts",
"store.url": "http://minio:9000"
}
}

27
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@ -1,27 +0,0 @@
{
"name": "sink-s3-json",
"config": {
"timestamp.extractor": "Record",
"locale": "US",
"timezone": "UTC",
"connector.class": "io.confluent.connect.s3.S3SinkConnector",
"tasks.max": "1",
"s3.region": "ap-southeast-1",
"s3.bucket.name": "dev-data-raw",
"s3.part.size": "5242880",
"flush.size": "10",
"rotate.interval.ms": "3600000",
"rotate.schedule.interval.ms": "3000",
"storage.class": "io.confluent.connect.s3.storage.S3Storage",
"format.class": "io.confluent.connect.s3.format.json.JsonFormat",
"partitioner.class": "io.confluent.connect.storage.partitioner.TimeBasedPartitioner",
"path.format": "'year'=YYYY/'month'=M/'day'=d",
"partition.duration.ms": "3600000",
"schema.generator.class": "io.confluent.connect.storage.hive.schema.DefaultSchemaGenerator",
"schema.compatibility": "NONE",
"name": "sink-s3-json",
"topics.regex": "json.*",
"store.url": "http://minio:9000",
"topics.dir": "sink-s3-json"
}
}

412
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@ -1,412 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 4 Kinesis Data Streams & Firehose\n",
"\n",
"Przebieg ćwiczenia\n",
"* Stwórz Data Stream z wykorzystaniem boto3 / AWS console (GUI)\n",
"* wygeneruj testowe dane do streama\n",
"* odczytaj dane ze streama (ShardIterator)\n",
"* Stwórz Kinesis Firehose Stream i podepnij pod niego utworzony wcześniej Data Stream jako source. Skonfiguruj buffor size = 1Mb buffor time = 60s\n",
"* wygeneruj 10000 wiadomości i sprawdź czy dane ładowane są do S3\n",
"\n",
"## Pamiętaj aby po skończonych ćwiczeniach usunąć wszystkie obiekty\n",
"### Uwaga !!! poniższy skrypt tworzy obiekty w regionie HongKong ! Na końcu skryptu jest funkcja tear_down_all() która usuwa testowy bucket, bazę Glue i Kinesis Data Streams czyli wszystkie obiekty które były stworzone w kodzie."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import boto3\n",
"\n",
"REGION = \"ap-east-1\"\n",
"\n",
"session_kwargs = {\n",
"\n",
" \"aws_access_key_id\":\"\",\n",
" \"aws_secret_access_key\":\"\",\n",
" \"aws_session_token\":\"\",\n",
" \"region_name\": REGION\n",
"}\n",
"\n",
"session = boto3.Session(**session_kwargs)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"kinesis_client = session.client(\"kinesis\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'ResponseMetadata': {'RequestId': 'dd209b3a-57d1-b862-8a48-bc194546845a',\n",
" 'HTTPStatusCode': 200,\n",
" 'HTTPHeaders': {'x-amzn-requestid': 'dd209b3a-57d1-b862-8a48-bc194546845a',\n",
" 'x-amz-id-2': 'k78aY4x6wCDEXo6kL76yEG64tV2ct9TQxM76Bfy345CJgSaVdfDJsjlr1jzNnpRxVk2qc+G9L42xZbmYrx/mivAFMWoek7Si',\n",
" 'date': 'Sat, 20 Jun 2020 15:01:55 GMT',\n",
" 'content-type': 'application/x-amz-json-1.1',\n",
" 'content-length': '0'},\n",
" 'RetryAttempts': 0}}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"STREAM_NAME = 'uam-test'\n",
"\n",
"kinesis_client.create_stream(\n",
" StreamName=STREAM_NAME,\n",
" ShardCount=1\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['uam-test']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kinesis_client.list_streams()[\"StreamNames\"]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'ShardId': 'shardId-000000000000', 'SequenceNumber': '49608139280302835973846909376978574930250627257427034114', 'ResponseMetadata': {'RequestId': 'c4858035-fc96-5621-93ed-a71b8bbdb95f', 'HTTPStatusCode': 200, 'HTTPHeaders': {'x-amzn-requestid': 'c4858035-fc96-5621-93ed-a71b8bbdb95f', 'x-amz-id-2': 'Z8hEKLIaCne7YPLWuDMTiFcAt7s1HJvfENE/2Oj7ARnuzjOsVNj+QyHpuIbM0tMucp7YZxLq2M65Xy/bgyyYeI2T5eJviuYA', 'date': 'Sat, 20 Jun 2020 15:02:06 GMT', 'content-type': 'application/x-amz-json-1.1', 'content-length': '110'}, 'RetryAttempts': 0}}\n"
]
}
],
"source": [
"response = kinesis_client.put_record(\n",
" StreamName=STREAM_NAME,\n",
" Data=b'{\"col1\" : \"this is my test json data\"}',\n",
" PartitionKey='1' \n",
")\n",
"print(response)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'kinesis_client' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-3-f9ad17c57d0b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkinesis_client\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdescribe_stream\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mStreamName\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mSTREAM_NAME\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'kinesis_client' is not defined"
]
}
],
"source": [
"response = kinesis_client.describe_stream(StreamName=STREAM_NAME) \n",
"print(response)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'response' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-4-bfebdd8c4299>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mshard_ids\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mstream_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mresponse\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;34m'StreamDescription'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mstream_name\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'StreamDescription'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'StreamName'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'response' is not defined"
]
}
],
"source": [
"shard_ids = []\n",
"stream_name = None \n",
"if response and 'StreamDescription' in response:\n",
" stream_name= response['StreamDescription']['StreamName'] \n",
" \n",
" for shard_id in response['StreamDescription']['Shards']:\n",
" shard_id = shard_id['ShardId']\n",
" shard_iterator = kinesis_client.get_shard_iterator(StreamName=stream_name, ShardId = shard_id, ShardIteratorType=\"TRIM_HORIZON\")\n",
" shard_ids.append({'shard_id' : shard_id ,'shard_iterator' : shard_iterator['ShardIterator'] })\n",
" \n",
"shard_ids"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'AAAAAAAAAAHfJPwIwOEqHzQIjn90snM/nPs4zZARsJlXPyGlUTbvU+T5cdGvXzb54qetks+heTq/ttfFlehkcLGr27CpkPNDn2A9NHYc1w+3VjLIBmNKTLJlHnCjjFCwgqksrs1mUQVli12hZjy6wZXhGualZUI//H2BxRwKqH/Pf2Zk9S6KSbeJFDm0boV2COPqB3wZ21axe8lWXJVJAfjMgPacIU6K'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sh = shard_iterator[\"ShardIterator\"]\n",
"sh"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"tries = 0\n",
"limit = 100\n",
"result = []\n",
"while tries < 10:\n",
" tries += 1\n",
" response_get_rec = kinesis_client.get_records(ShardIterator = sh , Limit = limit)\n",
" shard_iterator = response_get_rec['NextShardIterator']\n",
" break\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Records': [{'SequenceNumber': '49608139280302835973846909376978574930250627257427034114',\n",
" 'ApproximateArrivalTimestamp': datetime.datetime(2020, 6, 20, 17, 2, 6, 976000, tzinfo=tzlocal()),\n",
" 'Data': b'{\"col1\" : \"this is my test json data\"}',\n",
" 'PartitionKey': '1'}],\n",
" 'NextShardIterator': 'AAAAAAAAAAEgmwgBEgaauGNF/YzN5S+FcuWOWZZMledH4BR7CLiGD9iYYL4z+eLK7NaTQTHTAlSFEYm6N6vtjdFcTl8ibGJGKnuQthZiMgCfolA1FAAoWmLHvI0slHvZx1oWLfdApD8robDWj3zX/2d4zOzj1P9xz/+Xo8/YFdCXd0ENfUNxI7MhzZUGamw09rXa8Y0sDunFpkLy7msr5vjURGjr+xrf',\n",
" 'MillisBehindLatest': 0,\n",
" 'ResponseMetadata': {'RequestId': 'e580a084-b06e-ca24-b2e8-87b6c745255a',\n",
" 'HTTPStatusCode': 200,\n",
" 'HTTPHeaders': {'x-amzn-requestid': 'e580a084-b06e-ca24-b2e8-87b6c745255a',\n",
" 'x-amz-id-2': '/zy9vbX4wzEuqd7TZ959MQcL0OzB9kPQ3TSfrtEIIpI1IvubKb7OgnxYhxiWIZpvPWbtzXO5x0r+eO6+A8wR+bn0v8khnlpL',\n",
" 'date': 'Sat, 20 Jun 2020 15:02:10 GMT',\n",
" 'content-type': 'application/x-amz-json-1.1',\n",
" 'content-length': '489'},\n",
" 'RetryAttempts': 0}}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"response_get_rec"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['49608122819498384072835111163875039160761192199284064258']"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[x[\"SequenceNumber\"] for x in response_get_rec[\"Records\"] ]"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'SequenceNumber': '49608122819498384072835111163875039160761192199284064258',\n",
" 'ApproximateArrivalTimestamp': datetime.datetime(2020, 6, 20, 4, 13, 11, 39000, tzinfo=tzlocal()),\n",
" 'Data': b'{\"col1\" : \"this is my test json data\"}',\n",
" 'PartitionKey': '1'}]"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"response_get_rec[\"Records\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Utworz recznie Kinesis Firehose dla tego Stream'a dopiero pozniej wygeneruj dane testowe ponizsza petla"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-13-0e9e1b83eba0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m response = kinesis_client.put_record(\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mStreamName\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mSTREAM_NAME\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mData\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mb'{\"col1\" : \"this is json data\"}'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mPartitionKey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'1'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/uam-d/lib/python3.8/site-packages/botocore/client.py\u001b[0m in \u001b[0;36m_api_call\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 314\u001b[0m \"%s() only accepts keyword arguments.\" % py_operation_name)\n\u001b[1;32m 315\u001b[0m \u001b[0;31m# The \"self\" in this scope is referring to the BaseClient.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 316\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_api_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moperation_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 317\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 318\u001b[0m \u001b[0m_api_call\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpy_operation_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/uam-d/lib/python3.8/site-packages/botocore/client.py\u001b[0m in \u001b[0;36m_make_api_call\u001b[0;34m(self, operation_name, api_params)\u001b[0m\n\u001b[1;32m 619\u001b[0m \u001b[0mhttp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparsed_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevent_response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 620\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 621\u001b[0;31m http, parsed_response = self._make_request(\n\u001b[0m\u001b[1;32m 622\u001b[0m operation_model, request_dict, request_context)\n\u001b[1;32m 623\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/uam-d/lib/python3.8/site-packages/botocore/client.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, operation_model, request_dict, request_context)\u001b[0m\n\u001b[1;32m 639\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_make_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moperation_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest_context\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 640\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 641\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_endpoint\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moperation_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 642\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 643\u001b[0m self.meta.events.emit(\n",
"\u001b[0;32m~/opt/anaconda3/envs/uam-d/lib/python3.8/site-packages/botocore/endpoint.py\u001b[0m in \u001b[0;36mmake_request\u001b[0;34m(self, operation_model, request_dict)\u001b[0m\n\u001b[1;32m 100\u001b[0m logger.debug(\"Making request for %s with params: %s\",\n\u001b[1;32m 101\u001b[0m operation_model, request_dict)\n\u001b[0;32m--> 102\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moperation_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcreate_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moperation_model\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/uam-d/lib/python3.8/site-packages/botocore/endpoint.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, request_dict, operation_model)\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[0mrequest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moperation_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0mcontext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequest_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'context'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 134\u001b[0;31m success_response, exception = self._get_response(\n\u001b[0m\u001b[1;32m 135\u001b[0m request, operation_model, context)\n\u001b[1;32m 136\u001b[0m while self._needs_retry(attempts, operation_model, request_dict,\n",
"\u001b[0;32m~/opt/anaconda3/envs/uam-d/lib/python3.8/site-packages/botocore/endpoint.py\u001b[0m in \u001b[0;36m_get_response\u001b[0;34m(self, request, operation_model, context)\u001b[0m\n\u001b[1;32m 164\u001b[0m \u001b[0;31m# If an exception occurs then the success_response is None.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[0;31m# If no exception occurs then exception is None.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m success_response, exception = self._do_get_response(\n\u001b[0m\u001b[1;32m 167\u001b[0m request, operation_model)\n\u001b[1;32m 168\u001b[0m kwargs_to_emit = {\n",
"\u001b[0;32m~/opt/anaconda3/envs/uam-d/lib/python3.8/site-packages/botocore/endpoint.py\u001b[0m in \u001b[0;36m_do_get_response\u001b[0;34m(self, request, operation_model)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[0mhttp_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfirst_non_none_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 199\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhttp_response\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m \u001b[0mhttp_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 201\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mHTTPClientError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 202\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/uam-d/lib/python3.8/site-packages/botocore/endpoint.py\u001b[0m in \u001b[0;36m_send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_send\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 269\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhttp_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 271\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/uam-d/lib/python3.8/site-packages/botocore/httpsession.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 253\u001b[0m \u001b[0mrequest_target\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_request_target\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproxy_url\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 254\u001b[0;31m urllib_response = conn.urlopen(\n\u001b[0m\u001b[1;32m 255\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 256\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest_target\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/uam-d/lib/python3.8/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 669\u001b[0m \u001b[0;31m# Make the request on the httplib connection object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 670\u001b[0;31m httplib_response = self._make_request(\n\u001b[0m\u001b[1;32m 671\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 672\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/uam-d/lib/python3.8/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 424\u001b[0m \u001b[0;31m# Python 3 (including for exceptions like SystemExit).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 425\u001b[0m \u001b[0;31m# Otherwise it looks like a bug in the code.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 426\u001b[0;31m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 427\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSocketError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 428\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_timeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mread_timeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/uam-d/lib/python3.8/site-packages/urllib3/packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/uam-d/lib/python3.8/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 419\u001b[0m \u001b[0;31m# Python 3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 420\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 421\u001b[0;31m \u001b[0mhttplib_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 422\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 423\u001b[0m \u001b[0;31m# Remove the TypeError from the exception chain in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/uam-d/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1330\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1331\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1332\u001b[0;31m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1333\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1334\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/uam-d/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mbegin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 301\u001b[0m \u001b[0;31m# read until we get a non-100 response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 302\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 303\u001b[0;31m \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 304\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/uam-d/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36m_read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 262\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 263\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 264\u001b[0;31m \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 265\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 266\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mLineTooLong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"status line\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/uam-d/lib/python3.8/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m 667\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 668\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 669\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 670\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 671\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/uam-d/lib/python3.8/ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m 1239\u001b[0m \u001b[0;34m\"non-zero flags not allowed in calls to recv_into() on %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1240\u001b[0m self.__class__)\n\u001b[0;32m-> 1241\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1242\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1243\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/opt/anaconda3/envs/uam-d/lib/python3.8/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m 1097\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1099\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1100\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1101\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"for i in range(0,10000):\n",
" response = kinesis_client.put_record(\n",
" StreamName=STREAM_NAME,\n",
" Data=b'{\"col1\" : \"this is json data\"}',\n",
" PartitionKey='1' \n",
" )\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"BUCKET_NAME = 'datalake-uam'\n",
"GLUE_DB = 'uam'\n",
"KINESIS_STREAM = 'uam-test'\n",
"KINESIS_FIREHOSE = 'uam-test-fh'\n",
"\n",
"s3_client = session.client(\"s3\")\n",
"glue_client = session.client(\"glue\")\n",
"\n",
"def tear_down_all():\n",
" \n",
" s3 = boto3.resource('s3',**session_kwargs)\n",
" bucket = s3.Bucket(BUCKET_NAME)\n",
" bucket.objects.delete()\n",
" \n",
" s3_client.delete_bucket(\n",
" Bucket = BUCKET_NAME\n",
" )\n",
" \n",
" glue_client.delete_database(\n",
" Name=GLUE_DB\n",
" )\n",
" \n",
" kinesis_client.delete_stream(StreamName=STREAM_NAME)\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"tear_down_all()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

262
przetwarzanie_danych_w_chmurze/jupyter/UAM_Lab_1_reading_from_kinesis_stream.ipynb
View File

@ -1,262 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "alternate-pantyhose",
"metadata": {},
"source": [
"# Lab1 Czytanie Kinesis Data Streams \n",
"\n",
"Przebieg ćwiczenia\n",
"* Stwórz Data Stream \n",
"* wygeneruj testowe dane do streama\n",
"* odczytaj dane ze streama (ShardIterator)\n",
"* zwróć uwagę na iteracje po shardach i iteratorach (per shard)\n",
"* porównaj przeczytane dane z danymi wygenerowanymi (czytamy dwie iteracje - pierwsze 10 rekordów TRIM_HORIZON)\n",
"* sprawdź jakie inne opcje ustawienia punktu w shardzie są"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "gross-series",
"metadata": {},
"outputs": [],
"source": [
"import boto3\n",
"from pprint import pprint\n",
"\n",
"kinesis_client = boto3.client('kinesis')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "excited-latex",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['cryptostock-dev-100603781557-jk-12345']"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kinesis_client.list_streams()[\"StreamNames\"]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "excellent-address",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'cryptostock-dev-100603781557-jk-12345'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"STREAM_NAME = kinesis_client.list_streams()[\"StreamNames\"][0]\n",
"STREAM_NAME"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "attended-combat",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{'HashKeyRange': {'EndingHashKey': '340282366920938463463374607431768211455',\n",
" 'StartingHashKey': '0'},\n",
" 'SequenceNumberRange': {'StartingSequenceNumber': '49617445977150094507622122574044516561004852020651229186'},\n",
" 'ShardId': 'shardId-000000000000'}]\n"
]
}
],
"source": [
"response = kinesis_client.describe_stream(StreamName=STREAM_NAME) \n",
"pprint(response[\"StreamDescription\"][\"Shards\"])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "together-finance",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'shard_id': 'shardId-000000000000',\n",
" 'shard_iterator': 'AAAAAAAAAAEfLA4f5f+lMhjNfHXIXsKxQeP3dg79sVKKRiT+843gRXwSQsYRXeMIS4KwdRUjPdChkE2ZZGYSG3DeghHZi41DXOE0pNSdFHnqkePkBVIX2cN/9rbedZTgX/WXfNaL+sMUfdbYV6f9iQEtTtRAYN3bXfk5jUwIBvcgB1mQDRzdT1Or150vbf3LSlLtC7XlkK7HNZoGM1t577jseZTyvJ4+yeBOV73DQnSFnL/EPQvVdm+lidZtaNe39NMak4bXx5AWmhwblwLPmXg/l2PMDx7Z'}]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shard_ids = []\n",
"stream_name = None \n",
"if response and 'StreamDescription' in response:\n",
" stream_name= response['StreamDescription']['StreamName'] \n",
" \n",
" # reading all shards (getting shard iterators)\n",
" for shard_id in response['StreamDescription']['Shards']:\n",
" shard_id = shard_id['ShardId'] \n",
" shard_iterator = kinesis_client.get_shard_iterator(StreamName=stream_name, ShardId = shard_id, ShardIteratorType=\"TRIM_HORIZON\")\n",
" \n",
" si = shard_iterator[\"ShardIterator\"]\n",
" shard_ids.append({'shard_id' : shard_id ,'shard_iterator' : si })\n",
" \n",
"shard_ids"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "vital-bridges",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{'ApproximateArrivalTimestamp': datetime.datetime(2021, 4, 18, 14, 18, 25, 191000, tzinfo=tzlocal()),\n",
" 'Data': b'{\"transaction_ts\": 1601510403, \"symbol\": \"ETH_USD\", \"price\": 360'\n",
" b'.03, \"amount\": 0.646, \"dollar_amount\": 232.57938, \"type\": \"buy\",'\n",
" b' \"trans_id\": 124289044}\\n',\n",
" 'PartitionKey': 'ETH_USD',\n",
" 'SequenceNumber': '49617445977150094507622122578777461144796130256147709954'},\n",
" {'ApproximateArrivalTimestamp': datetime.datetime(2021, 4, 18, 14, 18, 25, 310000, tzinfo=tzlocal()),\n",
" 'Data': b'{\"transaction_ts\": 1601510403, \"symbol\": \"BTC_USD\", \"price\": 107'\n",
" b'80.83, \"amount\": 0.035, \"dollar_amount\": 377.32905, \"type\": \"buy'\n",
" b'\", \"trans_id\": 124289043}\\n',\n",
" 'PartitionKey': 'BTC_USD',\n",
" 'SequenceNumber': '49617445977150094507622122578778670070615744885322416130'},\n",
" {'ApproximateArrivalTimestamp': datetime.datetime(2021, 4, 18, 14, 18, 25, 428000, tzinfo=tzlocal()),\n",
" 'Data': b'{\"transaction_ts\": 1601510404, \"symbol\": \"ETH_USD\", \"price\": 360'\n",
" b'.12, \"amount\": 0.523, \"dollar_amount\": 188.34276, \"type\": \"buy\",'\n",
" b' \"trans_id\": 124289045}\\n',\n",
" 'PartitionKey': 'ETH_USD',\n",
" 'SequenceNumber': '49617445977150094507622122578779878996435359514497122306'},\n",
" {'ApproximateArrivalTimestamp': datetime.datetime(2021, 4, 18, 14, 18, 25, 545000, tzinfo=tzlocal()),\n",
" 'Data': b'{\"transaction_ts\": 1601510405, \"symbol\": \"BTC_USD\", \"price\": 107'\n",
" b'84.42, \"amount\": 0.25635676, \"dollar_amount\": 2764.65897, \"type\"'\n",
" b': \"buy\", \"trans_id\": 124289050}\\n',\n",
" 'PartitionKey': 'BTC_USD',\n",
" 'SequenceNumber': '49617445977150094507622122578781087922254974143671828482'},\n",
" {'ApproximateArrivalTimestamp': datetime.datetime(2021, 4, 18, 14, 18, 25, 663000, tzinfo=tzlocal()),\n",
" 'Data': b'{\"transaction_ts\": 1601510407, \"symbol\": \"BTC_USD\", \"price\": 107'\n",
" b'84.42, \"amount\": 0.23877038, \"dollar_amount\": 2575.000061, \"type'\n",
" b'\": \"buy\", \"trans_id\": 124289051}\\n',\n",
" 'PartitionKey': 'BTC_USD',\n",
" 'SequenceNumber': '49617445977150094507622122578782296848074588772846534658'}]\n"
]
}
],
"source": [
"limit = 5\n",
"response_get_rec = kinesis_client.get_records(ShardIterator = si , Limit = limit)\n",
"next_shard_iterator = response_get_rec['NextShardIterator']\n",
"pprint(response_get_rec[\"Records\"])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "alone-martial",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"AAAAAAAAAAGUetfZYhJAUbRmLdnxgHF2gXHQ+Yt8063YzfurEZ+Vdauri9LJ13JLrPqLIrBxeHRJ1GEBctxNJ4jYeB4Um/JNu4+2L5Jfa1Apl9s9y6f/5UMZlqIAFGvUPmW53Gj6MyauM9r7EWNBUBZCvrFQkHvC9fQwNYP3eyYm1xp4K9fcjBX90qUdnGmFU69bq+3BF5I7PXgPHitcwzJev6PqPLVny2SmhSHtnRF/Rogj00Xv+DtKo1/SBdVid3tyQ0e9tm4XrgttPfPIhNsJB3j57lpM\n",
"[{'ApproximateArrivalTimestamp': datetime.datetime(2021, 4, 18, 14, 18, 25, 812000, tzinfo=tzlocal()),\n",
" 'Data': b'{\"transaction_ts\": 1601510409, \"symbol\": \"BTC_USD\", \"price\": 107'\n",
" b'84.42, \"amount\": 1.01303547, \"dollar_amount\": 10924.99998, \"type'\n",
" b'\": \"buy\", \"trans_id\": 124289054}\\n',\n",
" 'PartitionKey': 'BTC_USD',\n",
" 'SequenceNumber': '49617445977150094507622122578783505773894203402021240834'},\n",
" {'ApproximateArrivalTimestamp': datetime.datetime(2021, 4, 18, 14, 18, 25, 930000, tzinfo=tzlocal()),\n",
" 'Data': b'{\"transaction_ts\": 1601510410, \"symbol\": \"BTC_USD\", \"price\": 107'\n",
" b'84.42, \"amount\": 0.26135077, \"dollar_amount\": 2818.516471, \"type'\n",
" b'\": \"buy\", \"trans_id\": 124289055}\\n',\n",
" 'PartitionKey': 'BTC_USD',\n",
" 'SequenceNumber': '49617445977150094507622122578784714699713818031195947010'},\n",
" {'ApproximateArrivalTimestamp': datetime.datetime(2021, 4, 18, 14, 18, 26, 48000, tzinfo=tzlocal()),\n",
" 'Data': b'{\"transaction_ts\": 1601510413, \"symbol\": \"ETH_USD\", \"price\": 360'\n",
" b'.39, \"amount\": 5.55416701, \"dollar_amount\": 2001.666249, \"type\":'\n",
" b' \"buy\", \"trans_id\": 124289059}\\n',\n",
" 'PartitionKey': 'ETH_USD',\n",
" 'SequenceNumber': '49617445977150094507622122578785923625533432660370653186'},\n",
" {'ApproximateArrivalTimestamp': datetime.datetime(2021, 4, 18, 14, 18, 26, 165000, tzinfo=tzlocal()),\n",
" 'Data': b'{\"transaction_ts\": 1601510414, \"symbol\": \"ETH_USD\", \"price\": 360'\n",
" b'.6, \"amount\": 13.855, \"dollar_amount\": 4996.113, \"type\": \"buy\", '\n",
" b'\"trans_id\": 124289071}\\n',\n",
" 'PartitionKey': 'ETH_USD',\n",
" 'SequenceNumber': '49617445977150094507622122578787132551353047358264836098'},\n",
" {'ApproximateArrivalTimestamp': datetime.datetime(2021, 4, 18, 14, 18, 26, 282000, tzinfo=tzlocal()),\n",
" 'Data': b'{\"transaction_ts\": 1601510415, \"symbol\": \"ETH_USD\", \"price\": 360'\n",
" b'.24, \"amount\": 6.32869733, \"dollar_amount\": 2279.849926, \"type\":'\n",
" b' \"sell\", \"trans_id\": 124289072}\\n',\n",
" 'PartitionKey': 'ETH_USD',\n",
" 'SequenceNumber': '49617445977150094507622122578788341477172661987439542274'}]\n"
]
}
],
"source": [
"print(next_shard_iterator)\n",
"response_get_rec = kinesis_client.get_records(ShardIterator = next_shard_iterator , Limit = limit)\n",
"pprint(response_get_rec[\"Records\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "analyzed-applicant",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

5
przetwarzanie_danych_w_chmurze/jupyter/requirements.txt
View File

@ -1,5 +0,0 @@
boto3==1.17.33
faker==6.6.2
avro-python3==1.9.1
pandavro==1.6.0
pyarrow==0.17.1

798
przetwarzanie_danych_w_chmurze/labs/terraform/terraform.tfstate
View File

@ -1,798 +0,0 @@
{
"version": 4,
"terraform_version": "1.8.1",
"serial": 181,
"lineage": "f3b7a1af-2e3c-0d11-9318-1c676be57243",
"outputs": {},
"resources": [
{
"mode": "managed",
"type": "aws_athena_workgroup",
"name": "athena_workgroup",
"provider": "provider[\"registry.terraform.io/hashicorp/aws\"]",
"instances": [
{
"schema_version": 0,
"attributes": {
"arn": "arn:aws:athena:us-east-1:401785806395:workgroup/development",
"configuration": [
{
"bytes_scanned_cutoff_per_query": 0,
"enforce_workgroup_configuration": true,
"engine_version": [
{
"effective_engine_version": "Athena engine version 3",
"selected_engine_version": "AUTO"
}
],
"execution_role": "",
"publish_cloudwatch_metrics_enabled": true,
"requester_pays_enabled": false,
"result_configuration": [
{
"acl_configuration": [],
"encryption_configuration": [],
"expected_bucket_owner": "",
"output_location": "s3://athena-results-401785806395-kp-s1201686/output/"
}
]
}
],
"description": "",
"force_destroy": true,
"id": "development",
"name": "development",
"state": "ENABLED",
"tags": null,
"tags_all": {}
},
"sensitive_attributes": [],
"private": "bnVsbA==",
"dependencies": [
"aws_s3_bucket.athena_results"
]
}
]
},
{
"mode": "managed",
"type": "aws_glue_catalog_database",
"name": "datalake_db_processed_zone",
"provider": "provider[\"registry.terraform.io/hashicorp/aws\"]",
"instances": [
{
"schema_version": 0,
"attributes": {
"arn": "arn:aws:glue:us-east-1:401785806395:database/datalake_processed_401785806395_kp_s1201686",
"catalog_id": "401785806395",
"create_table_default_permission": [
{
"permissions": [
"ALL"
],
"principal": [
{
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"provider": "provider[\"registry.terraform.io/hashicorp/aws\"]",
"instances": [
{
"schema_version": 0,
"attributes": {
"bucket": "datalake-raw-401785806395-kp-s1201686",
"eventbridge": false,
"id": "datalake-raw-401785806395-kp-s1201686",
"lambda_function": [],
"queue": [],
"topic": []
},
"sensitive_attributes": [],
"private": "bnVsbA==",
"dependencies": [
"aws_lambda_function.etl_post_processing",
"aws_lambda_layer_version.aws_wrangler",
"aws_lambda_permission.allow_bucket",
"aws_s3_bucket.raw_bucket"
]
}
]
}
],
"check_results": null
}

9
przetwarzanie_danych_w_chmurze/labs/terraform/terraform.tfstate.backup
View File

@ -1,9 +0,0 @@
{
"version": 4,
"terraform_version": "1.8.1",
"serial": 166,
"lineage": "f3b7a1af-2e3c-0d11-9318-1c676be57243",
"outputs": {},
"resources": [],
"check_results": null
}