Re-Turn (Norway)

Turn's online advertising campaigns produce petabytes of data. This data is composed of trillions of events, e.g. impressions, clicks, etc., spanning multiple years. In addition to a timestamp, each event includes hundreds of fields describing the user's attributes, campaign's attributes, attributes of where the ad was served, etc. Advertisers need advanced analytics to monitor their running campaigns' performance, as well as to optimize future campaigns. This involves slicing and dicing the data over tens of dimensions over arbitrary time ranges. Many of these queries need to power the web portal to provide reports and dashboards. For an interactive response time, they have to have tens of milliseconds latency. At Turn's scale of operations, no existing system was able to deliver this performance in a cost effective manner. Kodiak, a distributed analytical data platform for web-scale high-dimensional data, was built to serve this need. It relies on pre-computations to materialize thousands of views to serve these advanced queries. These views are partitioned and replicated across Kodiak's storage nodes for scalability and reliability. They are system maintained as new events arrive. At query time, the system auto-selects the most suitable view to serve each query. Kodiak has been used in production for over a year. It hosts 2490 views for over three petabytes of raw data serving over 200K queries daily. It has median and 99% query latencies of 8 ms and 252 ms respectively. Our experiments show that its query latency is 3 orders of magnitude faster than leading big data platforms on head-to-head comparisons using Turn's query workload. Moreover, Kodiak uses 4 orders of magnitude less resources to run the same workload.

Cluster computing has emerged as a key parallel processing platform for large scale data. All major internet companies use it as their major central processing platform. One of cluster computing's most popular examples is MapReduce and its open source implementation Hadoop. These systems were originally designed for batch and massive-scale computations. Interestingly, over time their production workloads have evolved into a mix of a small fraction of large and long-running jobs and a much bigger fraction of short jobs. This came about because these systems end up being used as data warehouses, which store most of the data sets and attract ad hoc, short, data-mining queries. Moreover, the availability of higher level query languages that operate on top of these cluster systems proliferated these ad hoc queries. Since existing systems were not designed for short, latency-sensistive jobs, short interactive jobs suffer from poor response times. In this paper, we present Piranha--a system for optimizing short jobs on Hadoop without affecting the larger jobs. It runs on existing unmodified Hadoop clusters facilitating its adoption. Piranha exploits characteristics of short jobs learned from production workloads at Yahoo! clusters to reduce the latency of such jobs. To demonstrate Piranha's effectiveness, we evaluated its performance using three realistic short queries. Piranha was able to reduce the queries' response times by up to 71%.