{"id":24531,"date":"2014-04-25T10:00:09","date_gmt":"2014-04-25T07:00:09","guid":{"rendered":"http:\/\/www.javacodegeeks.com\/?p=24531"},"modified":"2014-04-24T21:21:26","modified_gmt":"2014-04-24T18:21:26","slug":"the-7-log-management-tools-java-developers-should-know","status":"publish","type":"post","link":"https:\/\/www.javacodegeeks.com\/2014\/04\/the-7-log-management-tools-java-developers-should-know.html","title":{"rendered":"The 7 Log Management Tools Java Developers Should Know"},"content":{"rendered":"

\"blog_boat-1\"<\/a><\/p>\n

Splunk vs. Sumo Logic vs. LogStash vs. GrayLog vs. Loggly vs. PaperTrails vs.\u00a0Splunk>Storm<\/strong><\/h2>\n

Splunk, Sumo Logic, LogStash, GrayLog, Loggly, PaperTrails \u2013 did I miss someone? I\u2019m pretty sure I did. Logs are like fossil fuels \u2013 we\u2019ve been wanting to get rid of them for the past 20 years, but we\u2019re not quite there yet. Well, if that\u2019s the case I want a BMW!<\/p>\n

To deal with the growth of log data a host of log management & analysis tools have been built over the last few years to help developers and operations make sense of the growing data. I thought it\u2019d be interesting to look at our options and what are each tools\u2019 selling point, from a developer\u2019s standpoint<\/strong>.<\/p>\n

Splunk<\/a><\/h3>\n

As the biggest tool in this space, I decided to put Splunk in a category of its own. That\u2019s not to say it\u2019s the best tool for what you need, but more to give credit to a product who essentially created a new category.<\/p>\n

Pros<\/h4>\n

Splunk is probably the most feature rich solution in the space. It\u2019s got hundreds of apps (I counted 537<\/a>) to make sense of almost every format of log data, from security to business analytics to infrastructure monitoring. Splunk\u2019s search and charting tools are feature rich to the point that there\u2019s probably no set of data you can\u2019t get to through its UI or APIs.<\/p>\n

Cons<\/h4>\n

Splunk has two major cons. The first, that is more subjective, is that it\u2019s an on-premise solution which means that setup costs in terms of money and complexity are high. To deploy in a high-scale environment you will need to install and configure a dedicated cluster. As a developer, it\u2019s usually something you can\u2019t or don\u2019t want to do as your first choice.<\/p>\n

Splunk\u2019s second con is that it\u2019s expensive. To support a real-world application you\u2019re looking at tens of thousands of dollars, which most likely means you\u2019ll need sign offs from high-ups in your organization, and the process is going to be slow. If you\u2019ve got a new app and you want something fast that you can quickly spin up and ramp as things progress \u2013 keep reading.<\/p>\n

Some more enterprise log analyzers can be found here<\/a>.<\/p>\n

SaaS Log Analyzers<\/h2>\n

Sumo Logic<\/a><\/h3>\n

Sumo was founded as a SaaS version of Splunk, going so far as to imitate some of splunk\u2019s features and visuals early on. Having said that, SL has developed to a full fledged enterprise class log management solution.<\/p>\n

Pros<\/h4>\n

SL is chock-full of features to reduce, search and chart mass amounts of data. Out of all the SaaS log analyzers, it\u2019s probably the most feature rich. Also, being a SaaS offering it inherently means setup and ongoing operation are easier. One of Sumo Logic\u2019s main points of attraction is the ability to establish baselines and to actively notify you when key metrics change after an event such as a new version rollout or a breach attempt.

<\/div> <\/div><\/p>\n

Cons<\/h4>\n

This one is shared across all SaaS log analyzers, which is you need to get the data to the service to actually do something with it. This means that you\u2019ll be looking at possible GBs (or more) uploaded from your servers. This can create issues on multiple fronts –<\/p>\n

    \n
  1. As a developer, if you\u2019re logging sensitive or PII you need to make sure it\u2019s redacted.<\/li>\n
  2. There may be a lag between the time data is logged and the time it\u2019s visible to to the service.<\/li>\n
  3. There\u2019s additional overhead on your machines transmitting GBs of data, which really depends on your logging throughput.<\/li>\n<\/ol>\n

    Sumo\u2019s pricing is also not transparent<\/a>, which means you might be looking at a buying process which is more complex than swiping your team\u2019s credit card to get going.<\/p>\n

    Loggly<\/a><\/h3>\n

    Loggly is also a robust log analyzer, focusing on simplicity and ease of use for a devops audience.<\/p>\n

    \"Loggly\"<\/a><\/h2>\n

    Pros<\/h4>\n

    Whereas Sumo Logic has a strong enterprise and security focus, Loggly is geared more towards helping devops find and fix operational problems. This makes it very developer-friendly. Things like creating custom performance and devops dashboards are super-easy to do. Pricing is also transparent, which makes start of use easier.<\/p>\n

    Cons<\/h4>\n

    Don\u2019t expect Loggly to scale into a full blown infrastructure, security or analytics solution. If you need forensics or infrastructure monitoring you\u2019re in the wrong place. This is a tools mainly for devops to parse data coming from your app servers. Anything beyond that you\u2019ll have to build yourself.<\/p>\n

    PaperTrails<\/a><\/h3>\n

    PaperTrails is a simple way to look and search through logs from multiple machines, in one consolidated easy-to-use interface. Think of it like tailing<\/a> your log in the cloud, and you won\u2019t be too far off.<\/p>\n

    \"PaperTrails\"<\/a><\/h3>\n

    Pros<\/h4>\n

    PT is what it is. A simple way to look at log files from multiple machines in a singular view in the cloud. The UX itself is very similar to looking at a log on your machine, and so are the search commands. It aims to do something simple and useful, and does it elegantly. It\u2019s also very affordable<\/a>.<\/p>\n

    Cons<\/h4>\n

    PT is mostly text based. Looking for any advanced integrations, predictive or reporting capabilities? You\u2019re barking up the wrong tree.<\/p>\n

    Splunk>Storm<\/a><\/h3>\n

    This is Splunk\u2019s little (some may say step) SaaS brother. It\u2019s a pretty similar offering that\u2019s hosted on Splunk\u2019s servers.<\/p>\n

    Pros<\/h4>\n

    Storm lets you experiment with Splunk without having to install the actual software on-premise, and contains much of the features available in the full version.<\/p>\n

    Cons<\/h4>\n

    This isn\u2019t really a commercial offering, and you\u2019re limited in the amount of data you can send. It seems to be more of an online limited version of Splunk meant to help people test out the product without having to deploy first. A new service called Splunk Cloud<\/a> is aimed at providing a full-blown Splunk SaaS experience.<\/p>\n

    Open Source Analyzers<\/h2>\n

    Logstash<\/a><\/h3>\n

    Logstash is an open source tool for collecting and managing log files. It\u2019s part of an open-source stack which includes ElasticSearch for indexing and searching through data and Kibana for charting and visualizing data. Together they form a powerful Log management solution.<\/p>\n

    \"Logstash\"<\/a><\/p>\n

    Pros<\/h4>\n

    Being an open-source solution means you\u2019re inherently getting a lot of a control and a very good price. Logstash uses three mature and powerful components, all heavily maintained, to create a very robust and extensible<\/a> package. For an open-source solution it\u2019s also very easy to install and start using. We use Logstash and love it.<\/p>\n

    Cons<\/h4>\n

    As Logstash is essentially a stack, it means you\u2019re dealing with three different products. That means that extensibility also becomes complex. Logstash filters are written in Ruby, Kibana is pure javascript and ElasticSearch has its own REST API as well as JSON templates.<\/p>\n

    When you move to production, you\u2019ll also need to separate the three into different machines, which adds to the complexity.<\/p>\n

    Graylog2<\/a><\/h3>\n

    A fairly new player in the space, GL2 is an open-source log analyzer backed by MongoDB as well as ElasticSearch (similar to Logstash) for storing and searching through log errors. It\u2019s mainly focused on helping developers detect and fix errors in their apps.<\/p>\n

    Also in this category you can find fluentd<\/a> and Kafka<\/a> whose one of its main use-cases is also storing log data. Phew, so many choices!<\/p>\n

    Takipi for Logs<\/h2>\n

    \"logScreenshot\"<\/a><\/p>\n

    While this post is not about Takipi, I thought there\u2019s one feature it has which you might find relevant to all of this.<\/p>\n

    The biggest disadvantage in all log analyzers and log files in general, is that the right data has to be put there by you first. From a dev perspective, it means that if an exception isn\u2019t logged, or the variable data you need to understand why it happened isn\u2019t there, no log file or analyzer in the world can help you. Production debugging sucks!<\/p>\n

    One of the things we\u2019ve added to Takipi is the ability to jump into a recorded debugging session straight from a log file error. This means that for every log error you can see the actual source code and variable values at the moment of error. You can learn more about it here<\/a>.<\/p>\n

    This is one post where I would love to hear from you guys about your experiences with some of the tools mentioned (and some that I didn\u2019t). I\u2019m sure there are things you would disagree with or would like to correct me on \u2013 so go ahead, the comment section is below and I would love to hear from you.<\/p>\n

    \n\n\n\n
    Reference: <\/span><\/td>\nThe 7 Log Management Tools Java Developers Should Know<\/a> from our JCG partner<\/a> Tal Weiss at the Takipi <\/a> blog.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

    Splunk vs. Sumo Logic vs. LogStash vs. GrayLog vs. Loggly vs. PaperTrails vs.\u00a0Splunk>Storm Splunk, Sumo Logic, LogStash, GrayLog, Loggly, PaperTrails \u2013 did I miss someone? I\u2019m pretty sure I did. Logs are like fossil fuels \u2013 we\u2019ve been wanting to get rid of them for the past 20 years, but we\u2019re not quite there yet. …<\/p>\n","protected":false},"author":529,"featured_media":2386,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[15],"tags":[132],"class_list":["post-24531","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-software-development","tag-logging"],"yoast_head":"\nThe 7 Log Management Tools Java Developers Should Know<\/title>\n<meta name=\"description\" content=\"Splunk vs. Sumo Logic vs. LogStash vs. GrayLog vs. Loggly vs. PaperTrails vs.\u00a0Splunk>Storm Splunk, Sumo Logic, LogStash, GrayLog, Loggly, PaperTrails \u2013\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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