[1000508] ***** Incoming request from 10.244.2.168:3000 ***** [1000508] Origin Id: 87d4s-a98d7-9a8742jsd -> System ID: 1

Writing custom log parsers and filters is a feature integrated in most log monitoring tools — aka security information and event management (SIEM) integration. Such parsers help us store the log data in more organized formats and querying becomes a lot easier and quicker. Also, properly organized log data can be fed into the log monitoring and anomaly detection systems to proactively monitor the system and forecast future events, too. These tools are so advanced that they provide a great visual experience via interactive dashboards based on time series and by real-time event analysis based on log data and other sources.

Almost all the log monitoring tools include features to define custom thresholds at certain levels. When the system hits those levels, the monitoring tool will proactively detect them with the help of log data and notify SysAdmins via alarms, push notification APIs (e.g., Slack Audit Logs API), emails, etc. Also, they can be preconfigured to trigger automated processes like dynamic scaling, system backup, changeovers, etc. However, if you invest in commercial software for log monitoring purposes, make sure you do a proper analysis because, for most small to medium software systems, this can be overkill.

I would wake up first thing in the morning and take a long jog. I know that no one would eye me suspiciously. I know that the local police that usually patrols my small village in Geneva, Switzerland won’t think I was trying to get away after robbing someone’s house.

If you’re writing logs to a file, make sure they are stored in a separate disk that has no impact on the running application (e.g., you can take a dedicated location from a separate server and mount it to the application servers). Also, understand the log frequency and growth pattern of the log files. Make sure you have a log rotation policy with proper file-naming conventions (e.g., append a timestamp to the file name when creating each log file) to keep log files at a fine length and quantity. Also, there should be mechanisms to back up old logs to safe locations and clean up the log storages regularly. Based on the industry you operate in, you can decide the backup expiration time (usually a few months or years) and, at the end of that period, destroy all the past log files.

Each time a data-related operation happens, without blindly accepting its completion, make sure you always validate the end state with evidence. For instance, in general, when you create/update/delete a record in the database, it returns the count of records changed, along with the updated record. In such cases, always run validations on the expected counts or results. Another example is when you insert a record into a data structure (say a queue). Instead of blindly assuming the insert operation, always take the queue size and validate whether it was increased by one. (Suppose your system is concurrent, but the underlying queue does not support concurrent operations. In such scenarios, you can actually lose some records, and having something like length validations is the only way to detect such hidden errors in the code.)

In most cases, it is a legal requirement for systems to mask and/or encrypt the sensitive information of the users and the internal systems. Based on the industry you operate in and the geographical region you belong to, the compliance requirements may change. Therefore, do proper research and implement the correct procedures in your application. In some cases, before getting the application up and running, you may need to present the logging strategy to security authorities and get their approvals/certifications, too.

Errors and failures need best-effort inspections. To make that possible, the application must provide the domain experts of the system with sufficient background information and the business and technology contexts. For instance, if a request or message is failing to process, in addition to the error message, it is very helpful to log the failed request body, too.

Most enterprise systems operate as distributed computing platforms, and there are multiple instances of the same service with a variety of app configs, resources, versions, and network properties. To identify each one, it is recommended to assign an instance ID and use it during the inter-service communications.

Based on the deployment environment, the active log level also must be changed. The recommended convention for production environments is to print logs up to INFO level. In other environments, logs will be printed up to either DEBUG or TRACE level, according to the level of granularity preferred by the Dev and SysOps teams.

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e will not link the nodes that we have just arranged to be leveraged in our architecture. This task use to take me the longest when I needed to update or tweak an architecture. If you take a look below it is just a matter of defining the flow to each node with double arrows, and you are done! With this example we will just link the nodes without labels, but if you take a look at the documentation applying labels to your links is a pretty easy task.

[ec2-user@ip-XXX-XX-X-XXX logs]$ ls .. APIM_V2_I02-2020-11-20_04:38:43.log APIM_V2_I02-2020-11-23_02:05:35.log APIM_V2_I02-2020-11-24_04:38:17.log APIM_V2_I02-2020-11-27_03:28:37.log APIM_V2_I02-2020-11-27_12:06:45.log ...

2020-11-11 13:52:12 INFO app - XYZ Integration API Manager v2.0.0 2020-11-11 13:52:15 INFO app - Loading configurations.. 2020-11-11 13:52:18 INFO app - *** InstanceID API_V2_I02 2020-11-11 13:52:18 INFO app - *** BaseURL http://10.244.2.168:3000 2020-11-11 13:52:19 INFO app - *** LogLevel 04 (INFO) 2020-11-11 13:52:31 INFO app - Started listening...

2020-11-11 13:52:12 INFO app - XYZ Integration API Manager v2.0.0 2020-11-11 13:52:15 INFO app - Loading configurations.. 2020-11-11 13:52:18 INFO app - *** InstanceID APIM_V2_I02 2020-11-11 13:52:18 INFO app - *** BaseURL http://10.244.2.168:3000 2020-11-11 13:52:19 INFO app - *** LogLevel 05 (DEBUG)

[1000508] Request Body: { ”user_id”:”XXXXXXXXXX”, ”personal_details”:{ ”firstName”:”XXXXXXXXXX”, ”lastName”:”XXXXXXXXXX”, ”DOB”:”XXXXXXXXXX”, ”gender”:”Male”, ”proffessional”:”Software Architect”, ”industry”:”IT”, ”SSN”:”XXXXXXXXXX” }, ”address_history”:[ {”streetAdress”:”Street No 1″,”zipcode”:”XXXXX”,”state”:”CA”}, {”streetAdress”:”Street No 2″,”zipcode”:”XXXXX”,”state”:”NY”}, {”streetAdress”:”Street No 2″,”zipcode”:”XXXXX”,”state”:”AL”} ], ”card_info”:[ {”type”:”amex″,”card_number”:”XXXXXXXXX”,”credit_limit”:”XXXXX”}, {”type”:”visa″,”card_number”:”XXXXXXXXX”,”credit_limit”:”XXXXX”} ] }

Having a central, accessible server/location to aggregate the logs is a very common practice among enterprise software developers. Usually, these log aggregators keep track of not only the application logs but also the other log data such as device/OS logs (e.g. Linux Syslog), network/firewall logs, database logs, etc. Also, they decouple the log files from the application servers and let us store all log data in more secure, organized, and efficient formats for a longer period of time. In some industries (e.g. banking and finance), it is mandatory to keep these logs preserved both locally and in central storage to make it hard for intruders and cybercriminals to access both locations and delete evidence of their activity at the same time. Due to this log redundancy, the mismatch between two locations can suggest red flags and help us prevent breaches from going unnoticed.