Replay log files stored locally with ShadowReader load testing
Posted Updated  Yuki Sawa Project4 minutes read (About 621 words)

Replay log files stored locally with ShadowReader load testing

ShadowReader can parse logs stored locally and push it to S3, so that it can be replayed by the load testing Lambdas.

The only requirements are that:

  • Logs must be in a consistent format.
  • You must supply a RegEx to instruct the script of the log format.
  • You must supply the time format for the timestamps in the logs.

Below is an example of how to parse logs stored in the default Nginx log format

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log_format combined '$remote_addr - $remote_user [$time_local] '
                    '"$request" $status $body_bytes_sent '
                    '"$http_referer" "$http_user_agent"';

How to

First, save the below to a logs.txt file.

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10.168.166.132 - - [15/Mar/2019:04:12:24 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.168.78 - - [15/Mar/2019:04:12:31 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.166.132 - - [15/Mar/2019:04:12:39 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.168.78 - - [15/Mar/2019:04:12:46 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.166.132 - - [15/Mar/2019:04:12:54 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.168.78 - - [15/Mar/2019:04:13:01 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.166.132 - - [15/Mar/2019:04:13:09 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.168.78 - - [15/Mar/2019:04:13:16 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.166.132 - - [15/Mar/2019:04:13:24 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.168.78 - - [15/Mar/2019:04:13:31 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.166.132 - - [15/Mar/2019:04:13:39 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.168.78 - - [15/Mar/2019:04:13:46 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.166.132 - - [15/Mar/2019:04:13:54 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.168.78 - - [15/Mar/2019:04:14:01 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.166.132 - - [15/Mar/2019:04:14:09 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.168.78 - - [15/Mar/2019:04:14:16 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.166.132 - - [15/Mar/2019:04:14:24 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.168.78 - - [15/Mar/2019:04:14:31 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.166.132 - - [15/Mar/2019:04:14:39 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.168.78 - - [15/Mar/2019:04:14:46 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.166.132 - - [15/Mar/2019:04:14:54 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.168.78 - - [15/Mar/2019:04:15:01 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.166.132 - - [15/Mar/2019:04:15:09 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.168.78 - - [15/Mar/2019:04:15:16 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.166.132 - - [15/Mar/2019:04:15:24 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.168.78 - - [15/Mar/2019:04:15:31 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.166.132 - - [15/Mar/2019:04:15:39 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.168.78 - - [15/Mar/2019:04:15:46 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.166.132 - - [15/Mar/2019:04:15:54 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.168.78 - - [15/Mar/2019:04:16:01 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"
10.168.166.132 - - [15/Mar/2019:04:16:09 +0000] "GET / HTTP/1.1" 403 23 "-" "ELB-HealthChecker/2.0" "-"

Now run the local parser, parser.py via the terminal.
The RegEx capturing group for the timestamp field must be named timestamp in the RegEx provided.
There must be a RegEx capturing group named uri which captures the uri of the logged event.
The RegEx must be in the Python format.

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:param file: Name of log file to parse. Accepts wildcards.
:param app: Name of the application for the logs.
:param bucket: S3 bucket to store the parsed logs to, Ex: "my-bucket123"
:param timeformat: The format of the timestamp in the logs. Ex: 'DD/MMM/YYYY:HH:mm:ss ZZ'
                   Accepts the following tokens: https://pendulum.eustace.io/docs/#tokens
:param regex: Regex to use to parse the logs.
              Ex: '(?P<remote_addr>[\S]+) - (?P<remote_user>[\S]+) \[(?P<timestamp>.+)\] "(?P<req_method>.+) (?P<uri>.+) (?P<httpver>.+)" (?P<status>[\S]+) (?P<body_bytes_sent>[\S]+) "(?P<referer>[\S]+)" "(?P<user_agent>[\S]+)" "(?P<x_forwarded_for>[\S]+)"'

Run the local parser

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# inside the shadowreader directory
pip install -r requirements-local-parser.txt
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python3 parser.py logs.txt --app app1 --bucket my-bucket \
--timeformat 'DD/MMM/YYYY:HH:mm:ss ZZ' \
--regex '(?P<remote_addr>[\S]+) - (?P<remote_user>[\S]+) \[(?P<timestamp>.+)\] "(?P<req_method>.+) (?P<uri>.+) (?P<httpver>.+)" (?P<status>[\S]+) (?P<body_bytes_sent>[\S]+) "(?P<referer>[\S]+)" "(?P<user_agent>[\S]+)" "(?P<x_forwarded_for>[\S]+)"'
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Wildcard example for parsing multiple files
python3 parser.py /tmp/logs-2019* --app app1 --bucket my-bucket \
--timeformat 'DD/MMM/YYYY:HH:mm:ss ZZ' \
--regex '(?P<remote_addr>[\S]+) - (?P<remote_user>[\S]+) \[(?P<timestamp>.+)\] "(?P<req_method>.+) (?P<uri>.+) (?P<httpver>.+)" (?P<status>[\S]+) (?P<body_bytes_sent>[\S]+) "(?P<referer>[\S]+)" "(?P<user_agent>[\S]+)" "(?P<x_forwarded_for>[\S]+)"'

NOTE: The S3 bucket set in --bucket must be the same as the name of the deployed parsed_data_bucket in serverless.yml

You should see an output like below.

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5 minutes of traffic data was uploaded to S3.
Average requests/min: 6
Max requests/min: 8
Min requests/min: 2
Timezone found in logs: +00:00
To load test with these results, use the below parameters for the orchestrator in serverless.yml
==========================================
test_params: {
  "base_url": "http://$your_base_url",
  "rate": 100,
  "replay_start_time": "2019-03-15T04:12",
  "replay_end_time": "2019-03-15T04:16",
  "identifier": "oss"
}
apps_to_test: ["app1"]
==========================================

Paste the test_params and apps_to_test into serverless.yml and follow the other guides to start the load test.

Hands Free Canary with ALB Advanced Routing Rules
Posted Updated  Yuki Sawa Guide2 minutes read (About 316 words)

Hands Free Canary with ALB Advanced Routing Rules

Canary deployments may seem like an advanced technique that requires a team of engineers to implement.

But with the new Advanced Request Routing for ALBs (Application Load Balancer), safely releasing new versions of your application straight into production has never been easier.

First, either create a new ALB or use an existing ALB and copy its DNS name.
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Then, clone this repo https://github.com/ysawa0/alb-canary

And copy the DNS name to this section of serverless.yml

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environment:
  stage: ${self:custom.stage}
  region: ${self:custom.region}
  alb_dns_name: canary-alb-1183014609.us-east-1.elb.amazonaws.com

This repo will deploy a Lambda with a API Gateway endpoint that will redirect users to the ALB with a twist – it will add a ?id=$val GET parameter. Where $val will be an integer from 1 to 6.

It uses the Serverless Framework

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# Install Serverless if you don't have it
npm install serverless -g

Then run to deploy the Lambda and API Gateway

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sls deploy

Save the endpoint of the deployed API Gateway for later.
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Now, we will set up routing rules that will mimic our “application”.

Click View/edit rules

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Add the rules below
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It should now look like this.
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Now, trying querying the API Gateway endpoint we deployed earlier.

1 out of 6 times, it should be bucketed into the canary rule.

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curl -L https://dmkgpj2yxh.execute-api.us-east-1.amazonaws.com/qa/canary
Id was 1 through 5
curl -L https://dmkgpj2yxh.execute-api.us-east-1.amazonaws.com/qa/canary
Id is 6, you've been canaried!

That’s it! You’ve set up a canary deployment where 1/6 users are canaried.

AWSJar makes it easy to save data from AWS Lambda
Posted Updated  Yuki Sawa Project7 minutes read (About 983 words)

AWSJar makes it easy to save data from AWS Lambda

🏺 AWSJar

Downloads
Python 3.6

Jar Logo

🏺 AWSJar makes it easy to save data from AWS Lambda.

The data (either a dict, list, float, int, or string) can be saved within the Lambda itself as an environment variable or on S3.

Install

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pip install awsjar

Examples

Increment a sum with every invocation

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import awsjar

def lambda_handler(event, context):
    jar = awsjar.Jar(context.function_name)
    data = jar.get()  # Will return an empty dict if state does not already exist.

    s = data.get("sum", 0)
    data["sum"] = s + 1

    jar.put(data)

    return data

Make sure your website is up 24/7

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import awsjar
import requests

# Set a CloudWatch Event to run this Lambda every minute.
def lambda_handler(event, context):
    jar = awsjar.Jar(context.function_name)
    data = jar.get()  # Will return an empty dict if state does not already exist.

    last_status_code = data.get("last_status_code", 200)

    result = requests.get('http://example.com')
    cur_status_code = result.status_code

    if last_status_code != 200 and cur_status_code != 200:
        print('Website might be down!')

    jar.put({'last_status_code': cur_status_code})

Save data to S3

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import awsjar

# Save your data to an S3 object - s3://my-bucket/state.json
bkt = awsjar.Bucket('my-bucket', key='state.json')

data = {'num_acorns': 50, 'acorn_hideouts': ['tree', 'lake', 'backyard']}
bkt.put(data)

state = bkt.get()
>> {'num_acorns': 50, 'acorn_hideouts': ['tree', 'lake', 'backyard']}

How to

  1. Jar
    1. Initialization
    2. Save Data
    3. Serialize Data
    4. IAM Role for Lambda
  2. Bucket
    1. Initialization
    2. Save data
    3. Specifying Keys
    4. S3 Versioning
    5. Serialize Data

Jar

Save your data within the Lambda itself, as an environment variable.

This method has no associated costs but AWS only allows you to store up to 4KB of data in the environment variables.

Jar can compress the data before storing it, allowing up to about 8KB of uncompressed data.

This may not seem like much, but it can cover a lot of use cases. It’s also nice to not have to provision extra resources and keep everything self contained.
Here’s a 7KB list that will fit with Jar.

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x = list(range(1400))
>> [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399]
jar.put(x)

Initialization 

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import awsjar

# Cans specify region if testing locally
jar = awsjar.Jar(lambda_name='sams-lambda', region='us-east-1')

# If running the code in Lambda, it will automatically know the proper region it's running in.
jar = awsjar.Jar(lambda_name='sams-lambda')

# Turn on data compression
jar = awsjar.Jar(lambda_name='sams-lambda', compression=True)

Save data

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data = {'num_acorns': 50, 'acorn_hideouts': ['tree', 'lake', 'backyard']}
jar.put(data)

state = jar.get()
>> {'num_acorns': 50, 'acorn_hideouts': ['tree', 'lake', 'backyard']}

Serializing data

Jar comes with datetime encoders/decoders for you to use.

It uses the standard library json.dumps and json.loads to serialize data so it’s possible to write your own encoder/decoders to serialize your data.

Here’s some instructions

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from awsjar import Jar, datetime_decoder, datetime_encoder
from datetime import datetime

jar = Jar(
    lambda_name=lambda_name,
    region=region,
    decoder=datetime_decoder,
    encoder=datetime_encoder,
)
time = datetime.now()

data = {"list": [1, 2, 3], "dt1": time}

jar.put(data)
x = jar.get()
>> {"list": [1, 2, 3], 'dt1': datetime.datetime(2019, 1, 9, 18, 49, 44, 847202)}

IAM Role

Any Lambda using Jar to save to an env var will need these permissions specified in the Role.

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{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "VisualEditor0",
            "Effect": "Allow",
            "Action": [
                "lambda:UpdateFunctionConfiguration",
                "lambda:GetFunctionConfiguration"
            ],
            "Resource": "*"
        }
    ]
}

Bucket

Save your data to S3.

Initialization

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import awsjar

bkt = awsjar.Bucket(bucket='my-bucket', key='state.json')

# Can specify region if you'd like.
bkt = awsjar.Bucket(bucket='my-bucket', key='state.json', region='us-east-1')

# This will pretty print any data saved to S3.
bkt = awsjar.Bucket(bucket='my-bucket', key='state.json', pretty=True)

Save data

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data = {'num_acorns': 50, 'acorn_hideouts': ['tree', 'lake', 'backyard']}
bkt.put(data)

state = bkt.get()
>> {'num_acorns': 50, 'acorn_hideouts': ['tree', 'lake', 'backyard']}

bkt.delete()  # Delete the object
bkt.delete(key="key123")  # Delete the object

Specifying keys

You can specify the key to override the key that was used in initialization.

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bkt = aj.Bucket(bucket='my-bucket', key='state.json')
bkt.put(['test'])  # Saved to s3://my-bucket/state.json

data = ['override']
bkt.put(data, key="override.json")  # Saved to s3://my-bucket/override.json

state = bkt.get(key="override.json")
>> ['override']

Versioning

S3 has an eventual consistency data model

For example, this means that getting an object immediately after overwriting it may not return the data you expect.

To overcome this, enable versioning

If an S3 Bucket has versioning enabled, Bucket will detect it automatically and fetch the latest version of an object on any get() calls.

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# Check versioning status
bkt.is_versioning_enabled()

# Enable versioning
bkt.enable_versioning()

# Disable versioning
bkt.enable_versioning()

Serializing data

Same as Jar

Contributing

Please see the contributing guide for more specifics.

Contact / Support

Please use the Issues page

I greatly appreciate any feedback / suggestions! Email me at: yukisawa@gmail.com

License

Distributed under the Apache License 2.0. See LICENSE for more information.

How we fixed a Node.js memory leak by using ShadowReader to replay production traffic into QA
Posted Updated  Yuki Sawa Guidea few seconds read (About 85 words)

How we fixed a Node.js memory leak by using ShadowReader to replay production traffic into QA

A problem Edmunds faced recently was a memory leak in our Node.js application. It confounded the engineering team as it was only occurring in our production environment; we could not reproduce it in QA, until we introduced a new type of load testing tool developed here at Edmunds, which replays production traffic.

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load-test-animation

Read about it on opensource.com!

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