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How to use kinesis_api method in localstack

Best Python code snippet using localstack_python

consumer.py

Source:consumer.py

1"""2Author:3Date:4Python: 3.7.95Consumer for AWS Kinesis Stock Data Stream6"""7from datetime import datetime8import time9from kinesis_api import KinesisAPI, DynamoDbAPI10KINESIS_STREAM_NAME = "stock-stream"11KINESIS_SHARD_PARTITION_KEY = "stock"12DYNAMO_DB_TABLE = "stock-stream-data"13DYNAMO_DB_PARTITION_KEY = "symbol"14SYNAMO_DB_SORT_KEY = "minute"15def push_data():16    """17    Push to front end18    """19    pass20def insert_db(db_api, data):21    """22    Insert to database23    """24    print("Writing to dynamo DB")25    for row in data:26        db_api.put(row)27    return28def parse_record(data):29    """30    parse consumed records31    """32    required_keys = ["minute", "symbol", "open",33                     "high", "low", "close", "volume"]34    # get relavent keys35    parsed_data = {key: data[key] for key in required_keys}36    # Create array structure37    arrayed = []38    for _ in range(10):39        arrayed.append({key: '0' for key in required_keys})40    # Unpack dict and load to array41    for key, value in parsed_data.items():42        for subkey in sorted(value):43            arrayed[int(subkey) % 10][key] = value[subkey]44    return arrayed45def consume():46    api = KinesisAPI(stream_name=KINESIS_STREAM_NAME)47    db_api = DynamoDbAPI(DYNAMO_DB_TABLE)48    last_seq_num = ""49    for record in api.read_records(time_limit=1.0):50        data = record.get("data")51        last_seq_num = record.get("sequence_number")52        # print(f"\nDATA: {data}\nLAST_SEQ_NUM: {last_seq_num}\n")53        print("---------------------------")54        parsed_data = parse_record(data)55        print(parsed_data)56        insert_db(db_api, parsed_data)57        print("---------------------------")58    starttime = time.time()59    while True:60        print("Retrieving...")61        shard_iter = api.get_shard_iterator(62            iterator_type="AFTER_SEQUENCE_NUMBER",63            sequence_number=last_seq_num64        )65        for record in api.read_records(time_limit=1.0, shard_iterator=shard_iter):66            data = record.get("data")67            last_seq_num = record.get("sequence_number")68            # print(f"\nDATA: {data}\nLAST_SEQ_NUM: {last_seq_num}\n")69            print("---------------------------")70            parsed_data = parse_record(data)71            print(parsed_data)72            insert_db(db_api, parsed_data)73            print("---------------------------")74        print("Sleeping...")75        time.sleep(60.0 - ((time.time() - starttime) % 60.0))76if __name__ == "__main__":77    print("====================================")78    print("Stock Data Consumer for AWS Kinesis")79    print("====================================")...

server.py

Source:server.py

1from os import urandom, path2from flask import Flask, render_template, request, jsonify3from flask_socketio import SocketIO, emit4from flask_cors import CORS5from kinesis_api import DynamoDbAPI6from time import sleep7import pandas as pd8CUR_DIR = path.realpath(path.dirname(__file__))9DYNAMO_DB_TABLE = "stock-stream-data"10DYNAMO_DB_PARTITION_KEY = "symbol"11SYNAMO_DB_SORT_KEY = "minute"12app = Flask(__name__)13app.config["SECRET_KEY"] = urandom(32).hex14app.config["THREADED"] = True15app.config["DEBUG"] = True16CORS(app)17socketio = SocketIO(app, cors_allowed_origins="*")18db = DynamoDbAPI(table_name=DYNAMO_DB_TABLE)19@app.route("/")20def index():21    return render_template("index.html")22@app.route("/api/get_historical_data")23def get_historical_data():24    stock = request.args.get("stock")25    file_ = path.join(CUR_DIR, "data", f"hist-{stock}.csv")26    df = pd.read_csv(file_, index_col=0)27    return jsonify(df.to_dict(orient="records"))28@socketio.on("connect", namespace="/api/socket.io")29def on_connect():30    print("SocketIO: Connected!")31@socketio.on_error(namespace="/api/socket.io")32def error_handler(err):33    print(f"ERROR: {err}")34@socketio.on("get_live_data", namespace='/api/socket.io')35def get_live_data(symbol):36    print("Stock Symbol:", symbol)37    resp = db.get_all(38        projection_expr="symbol, #m, #o",39        expr_attr_names={"#m": "minute", "#o": "open"},40        filter="symbol = :stock",41        expr_attr_values={":stock": symbol}42    )43    labels = []44    data = []45    for item in resp["items"]:46        labels.append(item["minute"]["S"])47        data.append(float(item["open"]["N"]))48    emit(49        "graph_data",50        {"symbol": symbol, "labels": labels, "data": data},51        json=True,52        namespace="/api/socket.io"53    )54    sleep(60.0)55@socketio.on("disconnect", namespace="/api/socket.io")56def on_disconnect():57    print("SocketIO: Disconnected!")58if __name__ == "__main__":...

producer.py

Source:producer.py

1"""2Author: Maneesh Divana <maneeshd77@gmail.com>3Date: 2020-12-034Python: 3.7.95Reads stock data from CSV files and simulates stock streaming into AWS Kinesis Data Stream6"""7from os import path8from datetime import datetime9from time import sleep10import pandas as pd11from kinesis_api import KinesisAPI12CUR_DIR = path.realpath(path.dirname(__file__))13BASE_DIR = path.dirname(CUR_DIR)14DATA_DIR_ROOT = path.join(BASE_DIR, "data")15CSV_FILENAME = "intraday-22-oct-merged.csv"16KINESIS_STREAM_NAME = "stock-stream"17KINESIS_SHARD_PARTITION_KEY = "stock"18def simulate():19    """20    Simualte real time stream and post to Kinesis Data Stream Shard21    All companies at regular interval22    """23    # Load CSV into DataFrame24    df = pd.read_csv(path.join(DATA_DIR_ROOT, CSV_FILENAME))25    print(f"Shape of DataFrame: {df.shape}")26    print("DataFrame:")27    print(df, "\n")28    # Groups of 10 rows29    group = df.groupby(df.index // 10)30    # Connect to Kinesis using API31    api = KinesisAPI(stream_name=KINESIS_STREAM_NAME)32    print("-" * 64, "\n")33    start = datetime.now()34    print(f"[{start.strftime('%Y-%m-%d %H:%M:%S')}] Starting Kinesis producer...\n")35    # Send records for a group of 10 companies every 1 minute36    for idx, group in df.groupby(df.index // 10):37        now = datetime.now()38        print(f"[{now.strftime('%Y-%m-%d %H:%M:%S')}] Sending data:")39        print(group)40        api.write_record(41            data=group.to_dict(),42            partition_key=KINESIS_SHARD_PARTITION_KEY43        )44        print("")45        sleep(60.0)46    end = datetime.now()47    print(f"[{end.strftime('%Y-%m-%d %H:%M:%S')}] Finished producing. Exiting...\n")48    api.close()49if __name__ == "__main__":50    print("====================================")51    print("Stock Data Producer for AWS Kinesis")52    print("====================================")...

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