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.
e

Then, clone this repo https://github.com/ysawa0/alb-canary

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

1
2
3
4
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

1
2
# Install Serverless if you don't have it
npm install serverless -g

Then run to deploy the Lambda and API Gateway

1
sls deploy

Save the endpoint of the deployed API Gateway for later.
e

Now, we will set up routing rules that will mimic our “application”.

Click View/edit rules

e

Add the rules below
e
e

It should now look like this.
e

Now, trying querying the API Gateway endpoint we deployed earlier.

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

1
2
3
4
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

1
pip install awsjar

Examples

Increment a sum with every invocation

1
2
3
4
5
6
7
8
9
10
11
12
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

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
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

1
2
3
4
5
6
7
8
9
10
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.

1
2
3
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 

1
2
3
4
5
6
7
8
9
10
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

1
2
3
4
5
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

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
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.

1
2
3
4
5
6
7
8
9
10
11
12
13
14
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "VisualEditor0",
            "Effect": "Allow",
            "Action": [
                "lambda:UpdateFunctionConfiguration",
                "lambda:GetFunctionConfiguration"
            ],
            "Resource": "*"
        }
    ]
}

Bucket

Save your data to S3.

Initialization

1
2
3
4
5
6
7
8
9
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

1
2
3
4
5
6
7
8
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.

1
2
3
4
5
6
7
8
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.

1
2
3
4
5
6
7
8
# 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.

Using AWS ElastiCache Redis with Spinnaker
Posted Updated  Yuki Sawa Guide2 minutes read (About 304 words)

Using AWS ElastiCache Redis with Spinnaker

To productionalize a Spinnaker installation for high availability, one of the recommendations is to use an external Redis store, such as AWS ElasticCache. This guide will go over how to migrate a Kubernetes installation of Spinnaker to an AWS ElasticCache Redis instance using Halyard.

All config files (with the proper directory structure) used in this guide can be found in this this repo: ysawa0/spinnaker-elasticcache-redis

spinnaker logo elasticcache logo

Create the ElastiCache instance

elasticcache-settings

Keep Cluster Mode unchecked.

Node type will depend on your needs and budget, here we chose a m5.large

For Engine Version choose 3.2.10.

Configure Halyard and update Spinnaker

elasticcache-settings

After the instance is created, copy the Primary Endpoint for the cluster.

If you want to update all Spinnaker services at once, place this snippet into ~/.hal/default/service-settings/redis.yml, and replace $REDIS_PRIMARY_ENDPOINT with your endpoint.

1
2
overrideBaseUrl: redis://$REDIS_PRIMARY_ENDPOINT
skipLifeCycleManagement: true

To update each Spinnaker service at a time, place the below into ~/.hal/default/profile-settings/$SERVICE-local.yml

Where $SERVICE would be orca, clouddriver, gate, etc.

1
services.redis.baseUrl: redis://$REDIS_PRIMARY_ENDPOINT

Lastly, after updating the base URLs, place this into ~/.hal/default/profiles/gate-local.yml.

1
2
3
redis:
  configuration:
    secure: true

Now update Spinnaker by running hal deploy apply.

After you confirm that everything is working as expected, it’s time to disable the spin-redis service.

Update ~/.hal/default/service-settings/redis.yml by inserting enabled: false

1
2
3
overrideBaseUrl: redis://$REDIS_PRIMARY_ENDPOINT
skipLifeCycleManagement: true
enabled: false

And scale down the Redis Deployment to 0 replicas in Kubernetes.

1
kubectl scale deploy spin-redis -n spinnaker --replicas=0

Now sit back, relax and enjoy having to monitor one less data store.

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.

Shadow-reader-logo
load-test-animation

Read about it on opensource.com!

Recents

Categories

Tags

automation1
aws8
cicd2
cli1
envoy1
kubernetes3
networking1
performance-tests1
python3
serverless4
service-mesh2
spinnaker1

Archives

×