![]() ![]() Number of unique terms, for example the following line from a log fileįeb 21 05:13:10 lion named: unexpected RCODE (SERVFAIL) resolving Words - this will reduce the size of your index.Ĭonsider writing your own Analyzer to do tokenization to reduce the Write your own custom stop words list to skip indexing hugely common Regarding performance and index size for the one ferret document per logįile: By default, ferret only indexes the first 10,000 terms of eachĭocument so it might only be faster because it’s indexing less! Dittoįor the index file size :S See the :max_field_length option. (that’s usually only something done with the primary key from databases I’ve also found using a field as the the id is slooow, so avoid that Need to find logs down to the day, not the hour and minute)Īlso, disable term vectors, as this will save disk space. Reduce the number of unique terms in the index (maybe you’ll only ever Cut it down to the maximum accuracy you’ll need as this will Syslog files you’re indexing, parse the timestamp and convert it toĢ00802221816 format and add that as a separate untokenized field to the The timestamps) I’d recommend pre-parsing your log lines. Also consider :max_buffer_memory.Īs log files will often have lots of unique but “useless” terms (such as ![]() It set to flush to the index every 10,000 documents, but your log file An obvious one is ensuring auto_flush isĭisabled, but the next likely is :max_buffered_docs. Regarding performance of the one ferret document per line, you should ![]() I’ve been toying with the idea of a Ferret log indexer for my Linux This would have fast results for the more recentĪnd you would just have to be patient for the slightly older logs. Log files by line, like the last 2 days, and then do another set by file Haven’t implemented yet to get real numbers) is to index a certain To go about searching those logs? The best idea I can come up with Has anyone else tackled a problem like this and can offer any ideas on Of logs which comes to about 800ish Gb of log files. Your search terms, you have to crawl through each “hit” document to findįor the sake of full disclosure, at any given time we keep roughly 30 The downside is that after figuring out which files Relatively fast, 211sec for 2gb of logs, and the index size is a nice The second approach is indexing the log files as documents. This approach is not viable for indexing all of Long time and the index size is very large even when not storing theĬontents of the lines. ![]() Side to this is that doing a search will get you individual log lines as The first is loading each line in each log file as a “document”. My initial tests (on 2gb of log files) have been promising, I’ve taken Logs that involves specifying a date/time range and then grepping Right now we have a homemade system for searching through To inform the client about the result of the lookup, the protocol has a 4 bit field, called response code/RCODE.I’ve been exploring using ferret for indexing large amounts of The DNS protocol was designed to map domain names to IP addresses. This will be helpful in tracing DNS resolution errors and figuring out what went wrong behind the scenes. We recently released a new version of Cloudflare Resolver which adds a piece of information called “ Extended DNS Errors” (EDE) along with the response code under certain circumstances. ![]()
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