The rocket-fast system for log processing
Original post: Scalable and Flexible Elasticsearch Reindexing via rsyslog by @Sematext This recipe is useful in a two scenarios: migrating data from one Elasticsearch cluster to another (e.g. when you’re upgrading from Elasticsearch 1.x to 2.x or later) reindexing data from one index to another in a cluster pre 2.3. For clusters on version 2.3 […]
Original post: Monitoring rsyslog with Kibana and SPM by @Sematext A while ago we published this post where we explained how you can get stats about rsyslog, such as the number of messages enqueued, the number of output errors and so on. The point was to send them to Elasticsearch (or Logsene, our logging SaaS, […]
Original post: Recipe: rsyslog + Kafka + Logstash by @Sematext This recipe is similar to the previous rsyslog + Redis + Logstash one, except that we’ll use Kafka as a central buffer and connecting point instead of Redis. You’ll have more of the same advantages: rsyslog is light and crazy-fast, including when you want it […]
Original post: Recipe: Apache Logs + rsyslog (parsing) + Elasticsearch by @Sematext This recipe is about tailing Apache HTTPD logs with rsyslog, parsing them into structured JSON documents, and forwarding them to Elasticsearch (or a log analytics SaaS, like Logsene, which exposes the Elasticsearch API). Having them indexed in a structured way will allow you […]
Original post: Recipe: rsyslog + Redis + Logstash by @Sematext OK, so you want to hook up rsyslog with Logstash. If you don’t remember why you want that, let me give you a few hints: Logstash can do lots of things, it’s easy to set up but tends to be too heavy to put on […]
Originally posted on the Sematext blog: Monitoring rsyslog’s Performance with impstats and Elasticsearch If you’re using rsyslog for processing lots of logs (and, as we’ve shown before, rsyslog is good at processing lots of logs), you’re probably interested in monitoring it. To do that, you can use impstats, which comes from input module for process […]
Originally posted on the Sematext blog: Using Elasticsearch Mapping Types to Handle Different JSON Logs By default, Elasticsearch does a good job of figuring the type of data in each field of your logs. But if you like your logs structured like we do, you probably want more control over how they’re indexed: is time_elapsed […]
Original post: Recipe rsyslog+Elasticsearch+Kibana by @Sematext In this post you’ll see how you can take your logs with rsyslog and ship them directly to Elasticsearch (running on your own servers, or the one behind Logsene’s Elasticsearch API) in a format that plays nicely with Logstash. So you can use Kibana to search, analyze and make […]
by Micah Yoder, originally published on rackspace. Minor changes through Adiscon. There is a clear benefit to being able to aggregate logs from various servers and services into one place and be able to search them for any sort of arbitrary event. Traditional syslog can aggregate logs, but aggregating events from them sometimes involves grep and […]
Original post: Structured Logging with rsyslog and Elasticsearch via @sematext When your applications generate a lot of logs, you’d probably want to make some sense of them through searches and statistics. Here’s when structured logging comes in handy, and I would like to share some thoughts and configuration examples of how you could use a […]
