mertyildiran

var kflPcapS3Data = null;

wrapper.kflPcapS32 = function (data, params) {
  function kflPcapS3detect(data) {
    if (kflPcapS3Data === null)
      return;

    kflPcapS3Data.kflArr.forEach(function (kflQuery, idx) {
      if (kfl.match(kflQuery, data)) {
        kflPcapS3Data.pcapInfoArr[idx].pcapArr.push(data.stream);

mertyildiran

fatal error: unexpected signal during runtime execution
[signal SIGSEGV: segmentation violation code=0x1 addr=0x63 pc=0x7f124447b5ca]

runtime stack:
runtime.throw({0x1b8c38e, 0x7f11d8000d53})
	/usr/local/go/src/runtime/panic.go:1198 +0x71
runtime.sigpanic()
	/usr/local/go/src/runtime/signal_unix.go:719 +0x396

goroutine 659 [syscall]:

mertyildiran

FROM nginx:1.22.0-alpine

RUN rm -rf /etc/nginx/conf.d

RUN apk add --update nodejs
RUN apk add --update npm
RUN npm install -g runtime-env-cra@0.2.4

mertyildiran

package main

import (
	"fmt"
	"net/http"
	"strings"
)

// build with: go build -o main main.go

mertyildiran

Reproducing malformed HTTP packets

This is a slightly edited version of reassemblydump example of GoPacket.

Setup Sock Shop and its load tests in Google Kubernetes Engine.

Add and enter into sniffer pod:

kubectl run tapper-test -n sock-shop --rm -i --tty --overrides='{"spec": {"hostNetwork": true}}' --image mertyildiran/tapper-test -- /bin/bash

mertyildiran

Mizu Traffic Storage Solutions

In this Gist, three solutions will be proposed for the traffic storage in Mizu:

• Migrate to MongoDB, add a collection per protocol and an additional collection for TCP-level information. Indexes defined per protocol.
• Migrate to a log oriented solution like Elasticsearch/KQL.
• Develop a custom DBMS that's specialized in traffic storage, store dissected packets in binary files. (Let's say its name is Basenine)

Docker image size with SQLite: 85.5MB

mertyildiran

from confluent_kafka.admin import AdminClient, NewTopic

a = AdminClient({'bootstrap.servers': 'kafka:9092'})

new_topics = [NewTopic(topic, num_partitions=3, replication_factor=1) for topic in ["topic1", "topic2"]]
# Note: In a multi-cluster production scenario, it is more typical to use a replication_factor of 3 for durability.

# Call create_topics to asynchronously create topics. A dict
# of <topic,future> is returned.
print("CreateTopic")

mertyildiran

package main

import (
	"context"
	"fmt"
	"log"
	"time"

	"github.com/segmentio/kafka-go"
)

mertyildiran

---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: mizutest-amqp-py
  labels:
    name: mizutest-amqp-py
  namespace: sock-shop
spec:
  replicas: 1

mertyildiran

In this tutorial we'll explain two development workflows for Mizu:

• Standard development workflow that you build a Docker image, publish into Docker Hub and pull it to Kubernetes. The workflow is only necessary if you're developing some Kubernetes specific feature.
• Local machine development workflow that you run Mizu Agent on your machine with sudo privileges. Which is much faster than the standard development workflow and can be used if you're not developing a Kubernetes specific feature.

Standard Development Workflow

Whenever you make changes in the agent and tap packages or protocol extensions to test it; you first need to build a Docker image: