From 12e0dbce689b77a055708997918ef9750b3b5731 Mon Sep 17 00:00:00 2001 From: Fangjin Yang Date: Thu, 28 Mar 2013 19:12:10 -0700 Subject: [PATCH] fix broker datasources path --- .../com/metamx/druid/http/BrokerNode.java | 2 +- .../metamx/druid/http/ClientInfoResource.java | 2 +- publications/vldb/druid.pdf | Bin 0 -> 428586 bytes publications/vldb/druid.tex | 955 ++++++++++++++++++ 4 files changed, 957 insertions(+), 2 deletions(-) create mode 100644 publications/vldb/druid.pdf create mode 100644 publications/vldb/druid.tex diff --git a/client/src/main/java/com/metamx/druid/http/BrokerNode.java b/client/src/main/java/com/metamx/druid/http/BrokerNode.java index b09507ad6e9..39f7730fa3b 100644 --- a/client/src/main/java/com/metamx/druid/http/BrokerNode.java +++ b/client/src/main/java/com/metamx/druid/http/BrokerNode.java @@ -215,7 +215,7 @@ public class BrokerNode extends QueryableNode ); root.addEventListener(new GuiceServletConfig(injector)); - root.addFilter(GuiceFilter.class, "/datasources/*", 0); + root.addFilter(GuiceFilter.class, "/*", 0); for (String path : pathsForGuiceFilter) { root.addFilter(GuiceFilter.class, path, 0); diff --git a/client/src/main/java/com/metamx/druid/http/ClientInfoResource.java b/client/src/main/java/com/metamx/druid/http/ClientInfoResource.java index 8bf344e88bb..307d28447f6 100644 --- a/client/src/main/java/com/metamx/druid/http/ClientInfoResource.java +++ b/client/src/main/java/com/metamx/druid/http/ClientInfoResource.java @@ -42,7 +42,7 @@ import java.util.Set; /** */ -@Path("/datasources") +@Path("/druid/v2/datasources") public class ClientInfoResource { private static final int SEGMENT_HISTORY_MILLIS = 7 * 24 * 60 * 60 * 1000; // ONE WEEK diff --git a/publications/vldb/druid.pdf b/publications/vldb/druid.pdf new file mode 100644 index 0000000000000000000000000000000000000000..f639bee76f1311496010a5bf8215a2e48f4dc072 GIT binary patch literal 428586 zcma&NW0NjS(52h9ZJT!+yS>}CZQHhO+qP}n+-=)F&wFBGVj||tROE;H0hN)fuCAkEj 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b/publications/vldb/druid.tex @@ -0,0 +1,955 @@ +% THIS IS AN EXAMPLE DOCUMENT FOR VLDB 2012 +% based on ACM SIGPROC-SP.TEX VERSION 2.7 +% Modified by Gerald Weber +% Removed the requirement to include *bbl file in here. (AhmetSacan, Sep2012) +% Fixed the equation on page 3 to prevent line overflow. (AhmetSacan, Sep2012) + +\documentclass{vldb} +\usepackage{graphicx} +\usepackage{balance} % for \balance command ON LAST PAGE (only there!) +\usepackage{fontspec} +\setmainfont[Ligatures={TeX}]{Times} +\usepackage{hyperref} +\graphicspath{{figures/}} + +\hyphenation{metamarkets nelson} + +\begin{document} + +% ****************** TITLE **************************************** + +\title{Druid: Real-time Analytical Data Store} + +% possible, but not really needed or used for PVLDB: +%\subtitle{[Extended Abstract] +%\titlenote{A full version of this paper is available as\textit{Author's Guide to Preparing ACM SIG Proceedings Using \LaTeX$2_\epsilon$\ and BibTeX} at \texttt{www.acm.org/eaddress.htm}}} + +% ****************** AUTHORS ************************************** + +% You need the command \numberofauthors to handle the 'placement +% and alignment' of the authors beneath the title. +% +% For aesthetic reasons, we recommend 'three authors at a time' +% i.e. three 'name/affiliation blocks' be placed beneath the title. +% +% NOTE: You are NOT restricted in how many 'rows' of +% "name/affiliations" may appear. We just ask that you restrict +% the number of 'columns' to three. +% +% Because of the available 'opening page real-estate' +% we ask you to refrain from putting more than six authors +% (two rows with three columns) beneath the article title. +% More than six makes the first-page appear very cluttered indeed. +% +% Use the \alignauthor commands to handle the names +% and affiliations for an 'aesthetic maximum' of six authors. +% Add names, affiliations, addresses for +% the seventh etc. author(s) as the argument for the +% \additionalauthors command. +% These 'additional authors' will be output/set for you +% without further effort on your part as the last section in +% the body of your article BEFORE References or any Appendices. + +\numberofauthors{7} % in this sample file, there are a *total* +% of EIGHT authors. SIX appear on the 'first-page' (for formatting +% reasons) and the remaining two appear in the \additionalauthors section. + +\author{ +% You can go ahead and credit any number of authors here, +% e.g. one 'row of three' or two rows (consisting of one row of three +% and a second row of one, two or three). +% +% The command \alignauthor (no curly braces needed) should +% precede each author name, affiliation/snail-mail address and +% e-mail address. Additionally, tag each line of +% affiliation/address with \affaddr, and tag the +% e-mail address with \email. +% +% 1st. author +\alignauthor Fangjin Yang\\ +\affaddr{Metamarkets, Inc.}\\ +\affaddr{625 2nd Street, Suite 230}\\ +\affaddr{San Francisco, CA, USA}\\ +\email{fangjin@metamarkets.com} +\alignauthor Eric Tschetter\\ +\affaddr{Metamarkets, Inc.}\\ +\affaddr{625 2nd Street, Suite 230}\\ +\affaddr{San Francisco, CA, USA}\\ +\email{eric@metamarkets.com} +\alignauthor Gian Merlino\\ +\affaddr{Metamarkets, Inc.}\\ +\affaddr{625 2nd Street, Suite 230}\\ +\affaddr{San Francisco, CA, USA}\\ +\email{gian@metamarkets.com} +\and +\alignauthor Nelson Ray\\ +\affaddr{Metamarkets, Inc.}\\ +\affaddr{625 2nd Street, Suite 230}\\ +\affaddr{San Francisco, CA, USA}\\ +\email{nelson@metamarkets.com} +\alignauthor Xavier Léauté\\ +\affaddr{Metamarkets, Inc.}\\ +\affaddr{625 2nd Street, Suite 230}\\ +\affaddr{San Francisco, CA, USA}\\ +\email{xavier@metamarkets.com} +\alignauthor Deep Ganguli\\ +\affaddr{Metamarkets, Inc.}\\ +\affaddr{625 2nd Street, Suite 230}\\ +\affaddr{San Francisco, CA, USA}\\ +\email{deep@metamarkets.com} +} +% There's nothing stopping you putting the seventh, eighth, etc. +% author on the opening page (as the 'third row') but we ask, +% for aesthetic reasons that you place these 'additional authors' +% in the \additional authors block, viz. +\additionalauthors{Michael Driscoll (Metamarkets, \texttt{mike@metamarkets.com})} +\date{21 March 2013} +% Just remember to make sure that the TOTAL number of authors +% is the number that will appear on the first page PLUS the +% number that will appear in the \additionalauthors section. + +\maketitle + +\begin{abstract} +Druid is a scalable, real-time analytical data store that supports +ad-hoc queries on large-scale data sets. The system combines a +columnar data layout, a shared-nothing architecture, and an advanced +indexing structure to allow arbitrary exploration of billion-row +tables with sub-second latencies. Druid scales horizontally and is the +core engine of the Metamarkets platform. In this paper, we detail the +architecture and implementation of Druid and describe how it solves +the real-time data ingestion problem. +\end{abstract} + +\section{Introduction} +In recent years, enterprises are facing ever-growing collections of +data in all forms. The scale of data has reached terabytes, even +petabytes of information per day. Companies are increasingly realizing +the importance of unlocking the insights contained within this +data. Numerous database vendors such as IBM’s Netezza \cite{singh2011introduction}, Vertica +\cite{bear2012vertica}, and EMC’s Greenplum \cite{miner2012unified} offer data warehousing solutions, and +several research papers \cite{barroso2009datacenter, +chaudhuri1997overview, dewitt1992parallel} directly address this problem as +well. As the interactive data exploration space becomes more mature, +it is apparent that real-time ingestion and exploration of data will +unlock business-critical decisions for front office and back office +analysts. + +Metamarkets realized early on that for high data volumes, existing +Relational Database Management Systems (RDBMS) and most NoSQL +architectures were not sufficient to address the performance and +use-case needs of the business intelligence space. Druid was built to +address a gap we believe exists in the current big-data ecosystem for +a real-time analytical data store. Druid is a distributed, columnar, +shared-nothing data store designed to reliably scale to petabytes of +data and thousands of cores. It is a highly available system designed +to run on commodity hardware with no downtime in the face of failures, +data imports or software updates. We have been building Druid over the +course of the last two years and it is the core engine of the +Metamarkets technology stack. + +In many ways, Druid shares similarities with other interactive query systems +\cite{melnik2010dremel}, main-memory databases \cite{farber2012sap}, and widely-known distributed data +stores such as BigTable \cite{chang2008bigtable}, Dynamo \cite{decandia2007dynamo}, and Cassandra \cite{lakshman2010cassandra}. Unlike +most traditional data stores, Druid operates mainly on read-only data +and has limited functionality for writes. The system is highly optimized +for large-scale transactional data aggregation and arbitrarily deep data exploration. Druid is highly configurable +and allows users to adjust levels of fault tolerance and +performance. + +Druid builds on the ideas of other distributed data stores, real-time +computation engines, and search engine indexing algorithms. In this +paper, we make the following contributions to academia: +\begin{itemize} +\item We outline Druid’s real-time ingestion and query capabilities +and explain how we can explore events within milliseconds of their +creation +\item We describe how the architecture allows for fast and flexible +queries and how inverted indices can be applied to quickly filter +data +\item We present experiments benchmarking Druid’s performance +\end{itemize} +The format of the paper is as follows: Section \ref{sec:data-model} describes the Druid +data model. Section \ref{sec:cluster} presents an overview of the components of a +Druid cluster. Section \ref{sec:query-api} outlines the query API. Section \ref{sec:storage} describes +data storage format in greater detail. Section \ref{sec:robustness} discusses Druid +robustness and failure responsiveness. Section \ref{sec:benchmarks} provides our +performance benchmarks. Section \ref{sec:related} lists the related work and Section \ref{sec:conclusions} presents +our conclusions. + +\section{Data Model} +\label{sec:data-model} +The fundamental storage unit in Druid is the segment. Distribution and +replication in Druid are always done at a segment level. Segments +encapsulate a partition of a larger transactional data set. To better +understand how a typical row/column collection of data translates into +a Druid segment, consider the data set shown in Table~\ref{tab:sample_data}. + +\begin{table*} +\centering +\caption{Sample Druid data} +\label{tab:sample_data} +\texttt{ + \begin{tabular}{ l l l l l l l l l } + \hline + \textbf{Timestamp} & \textbf{Publisher} & \textbf{Advertiser} & \textbf{Gender} & \textbf{Country} & \textbf{Impressions} & \textbf{Clicks} & \textbf{Revenue} \\ + 2011-01-01T01:00:00Z & bieberfever.com & google.com & Male & USA & 1800 & 25 & 15.70 \\ + 2011-01-01T01:00:00Z & bieberfever.com & google.com & Male & USA & 2912 & 42 & 29.18 \\ + 2011-01-01T02:00:00Z & ultratrimfast.com & google.com & Male & USA & 1953 & 17 & 17.31 \\ + 2011-01-01T02:00:00Z & ultratrimfast.com & google.com & Male & USA & 3194 & 170 & 34.01 \\ + \end{tabular} +} +\end{table*} + +A segment is composed of multiple binary files, each representing a +column of a data set. The data set in Table~\ref{tab:sample_data} consists of 8 distinct +columns, one of which is the timestamp column. Druid always requires a +timestamp column because it (currently) only operates with event-based +data. Segments always represent some time interval and each column +file contains the specific values for that column over the time +interval. Since segments always contain data for a time range, it is +logical that Druid partitions data into smaller chunks based on the +timestamp value. In other words, segments can be thought of as blocks +of data that represent a certain granularity of time. For example, if +we wanted to shard the data in Table~\ref{tab:sample_data} to an hourly granularity, the +partitioning algorithm would result in two segments, one representing +each hour of 2011-01-01. Similarly, if we sharded the data to a daily +granularity, we would create a single segment for 2011-01-01. + +Partitioning the data based on granularity buckets allows users to +fine tune the degree of parallelization they want in Druid. A data set +representing a year’s worth of data may be bucketed by day, and a data +set representing only a day’s worth of day may be partitioned by +hour. Sharding on a single dimension (time) may still result in segments that are too large to manage if the data volume is sufficiently high. +To create more operable partition chunks, Druid may +additionally shard data based on other factors such as dimension +cardinality. Each shard creates a segment and hence, segments are uniquely identified by a data source +id describing the data, the time interval of the data, a version +indicating when the segment was created, and a shard partition number. + +Data in Druid can be conceptually thought of as being either real-time +or historical. Real-time data refers to recently ingested data; +typically, in a production setting, this will be data for the current +hour. Historical data refers to any data that is older. Segments can +be similarly classified as being either real-time or historical. + +Historical segments are immutable and do not support insert, delete, +or update semantics. By maintaining immutable blocks of data within +the system, we can maintain a consistent snapshot of historical +data and provide read consistency without having to worry about +concurrent updates and deletes. If updates to a historical segment are +required, we build a new segment for the same data source and time +interval with the updated data. This new segment will have an updated version identifier. + +Multiple segments for the same data source and time range +may exist in the system at any time. To provide read consistency, +Druid read operations always access data in a particular time range +from the segment with the latest version identifier for that time +range. Historical segments may be stored on local disk or in a +key-value “deep” store such as S3 \cite{decandia2007dynamo} or HDFS \cite{shvachko2010hadoop}. All historical +segments have associated metadata describing properties of the segment +such as size in bytes, compression format, and location in deep +storage. + +Real-time segments are mutable and generally represent a much shorter +duration of time than historical segments. Real-time segments contain +recent data and are incrementally populated as new events are +ingested. On a periodic basis, real-time segments are converted into +historical segments. Additional details about this conversion process +are given in Section~\ref{sec:realtime}. + +\section{Cluster} +\label{sec:cluster} +A Druid cluster consists of different types of nodes, each performing +a specific function. The composition of a Druid cluster is shown in +Figure~\ref{fig:druid_cluster}. + +\begin{figure*} +\centering +\includegraphics[width = 6in]{druid_cluster} +\caption{An overview of a Druid cluster.} +\label{fig:druid_cluster} +\end{figure*} + +Recall that data in Druid is classified as either real-time or +historical. The Druid cluster is architected to reflect this +conceptual separation of data. Real-time nodes are responsible for +ingesting, storing, and responding to queries for the most recent +events. Similarly, historical compute nodes are responsible for +loading and responding to queries for historical events. + +Data in Druid is stored on storage nodes. Storage nodes can be either +compute or real-time nodes. Queries to access this data will +typically first hit a layer of broker nodes. Broker nodes are +responsible for finding and routing queries down to the storage nodes +that host the pertinent data. The storage nodes compute their portion +of the query response in parallel and return their results to the +brokers. Broker nodes, compute nodes, and realtime nodes can all be +classified as queryable nodes. + +Druid also has a set of coordination nodes to manage load assignment, +distribution, and replication. Coordination nodes are not queryable +and instead focus on maintaining cluster stability. Coordination nodes +have an external dependency on a MySQL database. + +Druid relies on an Apache Zookeeper \cite{hunt2010zookeeper} cluster +for coordination. This dependency is required because there is no +direct coordination-related communication between Druid nodes. The +following sections will discuss each Druid component in greater +detail. + +\subsection{Apache Zookeeper} +Zookeeper is a service for coordinating processes of distributed +applications. Zookeeper provides connecting applications an +abstraction of a hierarchy of data nodes known as znodes. Each znode +is part of a hierarchical namespace, similar to file +systems. Zookeeper has the concept of ephemeral and permanent +znodes. Permanent nodes must be created and destroyed explicitly by a +connecting application. Ephemeral znodes can be created by connecting +applications and deleted either explicitly or if the session that +created the znode is terminated (such as in the event of service +failure). + +\subsection{Historical Compute Nodes} +Historical compute nodes are the main workers of a Druid cluster and +are self-contained and self-sufficient. Compute nodes load historical +segments from permanent/deep storage and expose them for +querying. There is no single point of contention between the nodes and +nodes have no knowledge of one another. Compute nodes are +operationally simple; they only know how to perform the tasks they are +assigned. To help other services discover compute nodes and the data +they hold, every compute node maintains a constant Zookeeper +connection. Compute nodes announce their online state and the segments +they serve by creating ephemeral nodes under specifically configured +Zookeeper paths. Instructions for a given compute node to load new +segments or drop existing segments are sent by creating ephemeral +znodes under a special “load queue” path associated with the compute +node. + +To expose a segment for querying, a compute node must first possess a +local copy of the segment. Before a compute node downloads a segment +from deep storage, it first checks a local disk directory (cache) to +see if the segment already exists in local storage. If no cache +information about the segment is present, the compute node will +download metadata about the segment from Zookeeper. This metadata +includes information about where the segment is located in deep +storage and about how to decompress and process the segment. Once a +compute node completes processing a segment, the node announces (in +Zookeeper) that it is serving the segment. At this point, the segment +is queryable. + +\subsubsection{Tiers} +\label{sec:tiers} +Compute nodes can be grouped in different tiers, where all nodes in a +given tier are identically configured. Different performance and +fault-tolerance parameters can be set for each tier. The purpose of +tiered nodes is to enable higher or lower priority segments to be +distributed according to their importance. For example, it is possible +to spin up a “hot” tier of compute nodes that have a high number of +cores and a large RAM capacity. The “hot” cluster can be configured to +download more frequently accessed segments. A parallel “cold” cluster +can also be created with much less powerful backing hardware. The +“cold” cluster would only contain less frequently accessed segments. + +\subsection{Real-time Nodes} +\label{sec:realtime} +Real-time nodes encapsulate the functionality to ingest and query +real-time data streams. Data indexed via these nodes is immediately +available for querying. Real-time nodes are a consumer of data and +require a corresponding producer to provide the data +stream. Typically, for data durability purposes, a message bus such as +Kafka \cite{kreps2011kafka} sits between the producer and the real-time node as shown +in Figure~\ref{fig:data-ingestion}. + +\begin{figure} +\centering +\includegraphics[width = 3in]{druid_message_bus} +\caption{Real-time data ingestion.} +\label{fig:data-ingestion} +\end{figure} + +The purpose of the message bus in Figure~\ref{fig:data-ingestion} is to act as a buffer for +incoming events. The message bus can maintain offsets indicating the +position in an event stream that a real-time node has read up to and +real-time nodes can update these offsets periodically. The message bus also acts as a backup storage for recent events. +Real-time nodes ingest data by reading events from the message bus. The time from event creation to message bus storage to +event consumption is on the order of hundreds of milliseconds. + +Real-time nodes maintain an in-memory index for all incoming +events. These indexes are incrementally populated as new events appear on the message bus. The indexes are also directly queryable. +Real-time nodes persist their indexes to disk either periodically or after some maximum row limit is +reached. After each persist, a real-time node updates the message bus +with the offset of the last event of the most recently persisted +index. Each persisted index is immutable. If a real-time node fails and recovers, it can simply reload +any indexes that were persisted to disk and continue reading the +message bus from the point the last offset was committed. Periodically committing offsets reduces the number of messages a real-time +node has to rescan after a failure scenario. + +Real-time nodes maintain a consolidated view of the currently updating +index and of all indexes persisted to disk. This unified view allows +all indexes on a node to be queried. On a periodic basis, the nodes will +schedule a background task that searches for all persisted indexes of +a data source. The task merges these indexes together and builds a +historical segment. The nodes will upload the segment to deep storage +and provide a signal for the historical compute nodes to begin serving +the segment. The ingest, persist, merge, and handoff steps are fluid; +there is no data loss as a real-time node converts a real-time segment +to a historical one. Figure~\ref{fig:data-durability} illustrates the process. + +\begin{figure} +\centering +\includegraphics[width = 3in]{druid_realtime_flow} +\caption{Real-time data durability} +\label{fig:data-durability} +\end{figure} + +Similar to compute nodes, real-time nodes announce segments in +Zookeeper. Unlike historical segments, real-time segments may +represent a period of time that extends into the future. For example, +a real-time node may announce it is serving a segment that contains +data for the current hour. Before the end of the hour, the real-time +node continues to collect data for the hour. Every 10 minutes (the +persist period is configurable), the node will flush and persist its +in-memory index to disk. At the end of the current hour, the real-time +node prepares to serve data for the next hour by creating a new index +and announcing a new segment for the next hour. The node does not +immediately merge and build a historical segment for the previous hour +until after some window period has passed. Having a window period +allows for straggling data points to come in and minimizes the risk of +data loss. At the end of the window period, the real-time node will +merge all persisted indexes, build a historical segment for the +previous hour, and hand the segment off to historical nodes to +serve. Once the segment is queryable on the historical nodes, the +real-time node flushes all information about the segment and +unannounces it is serving the segment. + +Real-time nodes are highly scalable. If the data volume and ingestion +rates for a given data source exceed the maximum capabilities of a +single node, additional nodes can be added. Multiple nodes can +consume events from the same stream, and every individual node only +holds a portion of the total number of events. This creates natural +partitions across nodes. Each node announces the real-time segment it +is serving and each real-time segment has a partition number. Data +from individual nodes will be merged at the Broker level. To our +knowledge, the largest production level real-time Druid cluster is +consuming approximately 2 TB of raw data per hour. + +\subsection{Broker Nodes} +Broker nodes act as query routers to other queryable nodes such as +compute and real-time nodes. Broker nodes understand the metadata +published in Zookeeper about what segments exist and on what nodes the +segments are stored. Broker nodes route incoming queries such that the queries hit +the right storage nodes. Broker nodes also merge partial results from +storage nodes before returning a final consolidated result to the +caller. Additionally, brokers provide an extra level of data +durability as they maintain a cache of recent results. In the event +that multiple storage nodes fail and all copies of a segment are +somehow lost, it is still possible that segment results can still be +returned if that information exists in the cache. + +\subsubsection{Timeline} +To determine the correct nodes to forward queries to, Broker nodes +first build a view of the world from information in Zookeeper. Recall +that Druid uses Zookeeper to maintain information about all compute +and real-time nodes in a cluster and the segments those nodes are +serving. For every data source in Zookeeper, the Broker node builds a +timeline of segments for the data source and the nodes that serve them. A timeline +consists of segments and represents which segments contain data for +what ranges of time. Druid may have multiple segments where the data +source and interval are the same but versions differ. The timeline +view will always surface segments with the most recent version +identifier for a time range. If two segments intervals overlap, the segment with the more recent +version always has precedence. When queries are received for a specific +data source and interval, the Broker node performs a lookup on the +timeline associated with the query data source for the query interval +and retrieves the segments that contain data for the query. The broker +node maps these segments to the storage nodes that serve them and +forwards the query down to the respective nodes. + + +\subsubsection{Caching} +\label{sec:caching} +Broker nodes employ a distributed cache with a LRU \cite{o1993lru, +kim2001lrfu} cache invalidation strategy. The broker cache stores +per segment results. The cache can be local to each broker node or +shared across multiple nodes using an external distributed cache +such as memcached \cite{fitzpatrick2004distributed}. Recall that each time a broker node receives a +query, it first maps the query to a set of segments. A subset of +these segment results may already exist in the cache and the results +can be directly pulled from the cache. For any segment results that +do not exist in the cache, the broker node will forward the query +to the compute nodes. Once the compute nodes return their results, +the broker will store those results in the cache. Real-time segments +are never cached and hence requests for real-time data will always +be forwarded to real-time nodes. Real-time data is perpetually +changing and caching the results would be unreliable. + + +\subsection{Coordination (Master) Nodes} +The Druid coordination or master nodes are primarily in charge of +segment management and distribution. The Druid master is responsible +for loading new segments, dropping outdated segments, managing segment +replication, and balancing segment load. Druid uses a multi-version +concurrency control swapping protocol for managing segments in order +to maintain stable views. + +The Druid master runs periodically to determine the current state of +the cluster. It makes decisions by comparing the expected state of the +cluster with the actual state of the cluster at the time of the +run. As with all Druid nodes, the Druid master maintains a connection +to Zookeeper for current cluster information. The master also +maintains a connection to a MySQL database that contains additional +operational parameters and configurations. One of the key pieces of +information located in the MySQL database is a segment table that +contains a list of historical segments that should be served. This +table can be updated by any service that creates historical +segments. The MySQL database also contains a rule table that governs +how segments are created, destroyed, and replicated in the cluster. + +The master does not directly communicate with a compute node when +assigning it work; instead the master creates an ephemeral znode in +Zookeeper containing information about what the compute node should +do. The compute node maintains a similar connection to Zookeeper to +monitor for new work. + +\subsubsection{Rules} +Historical segments are loaded and dropped from the cluster based on a +set of rules. Rules indicate how segments should be assigned to +different compute node tiers and how many replicates of a segment +should exist in each tier. Rules may also indicate when segments +should be dropped entirely from the cluster. The master loads a set of +rules from a rule table in the MySQL database. Rules may be specific +to a certain data source and/or a default set of rules can be +configured. The master will cycle through all available segments and +match each segment with the first rule that applies to it. + +\subsubsection{Load Balancing} +In a typical production environment, queries often hit dozens or even +hundreds of data segments. Since each compute node has limited +resources, historical segments must be distributed among the cluster +to ensure that the cluster load is not too imbalanced. Determining +optimal load distribution requires some knowledge about query patterns +and speeds. Typically, queries cover recent data spanning contiguous +time intervals for a single data source. On average, queries that +access smaller segments are faster. + +These query patterns suggest replicating recent historical segments at +a higher rate, spreading out large segments that are close in time to +different compute nodes, and co-locating segments from different data +sources. To optimally distribute and balance segments among the +cluster, we developed a cost-based optimization procedure that takes +into account the segment data source, recency, and size. The exact +details of the algorithm are beyond the scope of this paper. + +\section{Query API} +\label{sec:query-api} +Queries to Druid are made in the form of POST requests. All queryable +Druid nodes share the same query API. The body of the POST request is +a JSON object containing key-value pairs specifying various query +parameters. A typical query will contain the data source name, the +granularity of the result data, the time range of interest, and the +type of request. A sample time series query is shown below: +\begin{verbatim} +{ + "queryType" : "timeseries", + "dataSource" : "sample_data", + "intervals" : "2013-01-01/2013-01-02", + "filter" : { + "type" : "selector", + "dimension" : "poets", + "value" : "Ke$ha" + }, + "granularity" : "day", + "aggregations" : [ + { + "type" : "count", + "fieldName" : "row", + "name" : "row" + } + ] +} +\end{verbatim} + +Certain query types will also support a filter set. A filter set is an +arbitrary Boolean expression of dimension name and value +pairs. Support for complex nested filter sets enables flexibility and +provides the ability to deeply explore data. + +The exact query syntax depends on the query type and the information requested. +It is beyond the scope of this paper to describe the Query API in full detail. +We are also in the process of building out SQL support for Druid. + +\section{Storage} +\label{sec:storage} +Druid is a column-oriented data store. When considering aggregates +over a large number of events, the advantages storing data as columns +rather than rows are well documented \cite{cattell2011scalable}. Column storage allows for +more efficient CPU usage as only what is needed is actually loaded and +scanned. In a row oriented data store, all columns associated with a +row must be scanned as part of an aggregation. The additional scan +time can introduce performance degradations as high as 250\% \cite{bear2012vertica}. + +\subsection{Column Types} +Druid has multiple column types to represent the various column value +formats. Depending on the column type, different compression methods +are used to reduce the cost of storing a column in memory and on +disk. In the example given in Table~\ref{tab:sample_data}, the +publisher, advertiser, gender, and country columns only contain +strings. String columns can be dictionary encoded. Dictionary encoding +is a common method to compress data and has been used in other data +stores such as Powerdrill \cite{hall2012processing}. In the example in +Table~\ref{tab:sample_data}, we can map each publisher to an unique +integer identifier. +\begin{verbatim} +bieberfever.com -> 0 +ultratrimfast.com -> 1 +\end{verbatim} +This mapping allows us to represent the publisher column as an integer +array where the array indices correspond to the rows of the original +data set. For the publisher column, we can represent the unique +publishers as follows: +\begin{verbatim} +[0, 0, 1, 1] +\end{verbatim} + +The resulting integer array lends itself very well to +compression. Generic compression algorithms on top of encodings are +very common in column-stores. We opted to use the LZF \cite{liblzf2013} compression +algorithm. + +We can leverage similar compression algorithms for numeric +columns. For example, the clicks and revenue columns in +Table~\ref{tab:sample_data} can also be expressed as individual +arrays. +\begin{verbatim} +Clicks -> [25, 42, 17, 170] +Revenue -> [15.70, 29.18, 17.31, 34.01] +\end{verbatim} +In this case we compress the raw values as opposed to their dictionary +representations. + +\subsection{Filters} +To support arbitrary filter sets, Druid creates additional lookup +indices for string columns. These lookup indices are compressed and +Druid operates over the indices in their compressed form. Filters can +be expressed as Boolean equations of multiple lookup indices. Boolean +operations of indices in their compressed form is both performance and +space efficient. + +Let us consider the publisher column in +Table~\ref{tab:sample_data}. For each unique publisher in +Table~\ref{tab:sample_data}, we can form some representation +indicating which table rows a particular publisher is seen. We can +store this information in a binary array where the array indices +represent our rows. If a particular publisher is seen in a certain +row, that array index is marked as \texttt{1}. For example: +\begin{verbatim} +bieberfever.com -> rows [0, 1] -> [1][1][0][0] +ultratrimfast.com -> rows [2, 3] -> [0][0][1][1] +\end{verbatim} + +\texttt{\href{http://bieberfever.com}{bieberfever.com}} is seen in rows \texttt{0} and \texttt{1}. This mapping of column values +to row indices forms an inverted index \cite{tomasic1993performance}. If we want to know which +rows contain {\ttfamily bieberfever.com} or {\ttfamily ultratrimfast.com}, we can \texttt{OR} together +the \texttt{bieberfever.com} and \texttt{ultratrimfast.com} arrays. +\begin{verbatim} +[0][1][0][1] OR [1][0][1][0] = [1][1][1][1] +\end{verbatim} + +\begin{figure} +\centering +\includegraphics[width = 3in]{concise_plot} +\caption{Integer array size versus CONCISE set size.} +\label{fig:concise_plot} +\end{figure} + +This approach of performing Boolean operations on large bitmap sets is +commonly used in search engines. Bitmap compression algorithms are a +well-defined area of research and often utilize run-length +encoding. Well known algorithms include Byte-aligned Bitmap Code \cite{antoshenkov1995byte}, +Word-Aligned Hybrid (WAH) code \cite{wu2006optimizing}, and Partitioned Word-Aligned +Hybrid (PWAH) compression \cite{van2011memory}. Druid opted to use the CONCISE +algorithm \cite{colantonio2010concise} as it can outperform WAH by reducing the size of the +compressed bitmaps by up to 50\%. Figure~\ref{fig:concise_plot} illustrates the number of +bytes using CONCISE compression versus using an integer array. The +results were generated on a cc2.8xlarge system with a single thread, +2G heap, 512m young gen, and a forced GC between each run. The data +set is a single day’s worth of data collected from the Twitter garden +hose \cite{twitter2013} data stream. The data set contains 2,272,295 rows and 12 +dimensions of varying cardinality. As an additional comparison, we +also resorted the data set rows to maximize compression. + +In the unsorted case, the total CONCISE compressed size was 53,451,144 +bytes and the total integer array size was 127,248,520 bytes. Overall, +CONCISE compressed sets are about 42\% smaller than integer arrays. +In the sorted case, the total CONCISE compressed size was 43,832,884 +bytes and the total integer array size was 127,248,520 bytes. What is +interesting to note is that after sorting, global compression only +increased minimally. The total CONCISE set size to total integer array +size is 34\%. It is also interesting to note that as the cardinality +of a dimension approaches the total number of rows in a data set, +integer arrays actually require less space than CONCISE sets. + +\subsection{Storage Engine} +Druid’s persistence components allows for different storage engines to +be plugged in, similar to Dynamo \cite{decandia2007dynamo}. These storage engines may store +data in in-memory structures such as the JVM heap or in memory mapped +structures. The ability to swap storage engines allows for Druid to be +configured depending on a particular application’s specifications. An +in-memory storage engine may be operationally more expensive than a +memory-mapped storage engine but could be a better alternative if +performance is critical. At Metamarkets, we commonly use a +memory-mapped storage engine. + +\section{Robustness and Fault-Tolerance} +\label{sec:robustness} +To achieve high system availability and data durability, Druid employs +several fault recovery techniques. Druid has no single point of +failure. + +\subsection{Replication} +Druid replicates historical segments on multiple hosts. The number of +replicates in each tier of the historical compute cluster is fully +configurable. Setups that require high levels of fault tolerance can +be configured to have a high number of replicates. Replicates are +assigned to compute nodes by coordination nodes using the same load +distribution algorithm discussed in Section~\ref{sec:caching}. Conceptually, +broker nodes do not distinguish historical segments from their +replicates. Broker nodes forward queries to the first node it finds +that contains data for the query. + +Real-time segments follow a different replication model as real-time +segments are mutable. Recall that real-time nodes act as consumers of +a data stream. Multiple real-time nodes can read from the same message +bus if each maintains a unique offset, hence creating multiple copies +of a real-time segment. If a real-time node fails and recovers, it can +reload any indexes that were persisted to disk and read from the +message bus from the point it last committed an offset. + +\subsection{Local Segment Cache} +Recall that each Druid compute node maintains a local cache of +historical segments it recently served. A compute node also has a +lookup table for segments it has in its cache and stores this lookup +table on disk. When a compute node is assigned a new segment to load, +the compute node will first check its local segment cache directory to +see if the segment had been previously downloaded. If a cache entry +exists, the compute node will directly read the segment binary files +and load the segment. + +The segment cache is also leveraged when a compute node is initially +started. During startup, a compute node will first read its lookup +table to determine what segments it has cached. All cached segments +are immediately loaded and served. This feature introduces minimal +overhead and allows a compute node to readily serve data as soon as it +comes online. By making data quickly available on startup and +minimizing initial startup time, compute nodes that become +inexplicably disconnected from the cluster can reconnect themselves +seamlessly. This also means that software updates can be pushed to +compute nodes without disruption to cluster operations. In practice, a +software update to a compute node can be completed before coordination +nodes even notice that the compute node has disconnected. At +Metamarkets, we update Druid through rolling restarts. Compute nodes +are updated one at a time and we experience no downtime or data loss +through the update process. + +\subsection{Failure Detection} +If a compute node completely fails and becomes unavailable, the +ephemeral Zookeeper znodes it created are deleted. The master node +will notice that certain segments are insufficiently replicated or +missing altogether. Additional replicates will be created and +redistributed throughout the cluster. + +We are moving towards building out infrastructure to support +programmatic creation of real-time nodes. In the near future, the +master node will also notice if real-time segments are insufficiently +replicated and automatically create additional real-time nodes as +redundant backups. + +\subsection{Adding and Removing Nodes} +Starting and removing Druid nodes is a relatively simple process; all +that is required is to start and stop Java processes. There is minimal +operational overhead with adding nodes in batches. Scaling down the +cluster is usually done one node at a time with some time lapse +between shutdowns. This allows the master to have ample time to +redistribute load and create additional replicates. Shutting down +nodes in batches is generally not supported as it may destroy all +copies of a segment, which would lead to data loss. + +\section{Performance Benchmarks} +\label{sec:benchmarks} +To benchmark Druid performance, we created a large test cluster with +6TB of uncompressed data, representing tens of billions of fact +rows. The data set contained more than a dozen dimensions, with +cardinalities ranging from the double digits to tens of millions. We computed +four metrics for each row (counts, sums, and averages). The data was +sharded first on timestamp then on dimension values, creating +thousands of shards roughly 8 million fact rows apiece. + +The cluster used in the benchmark consisted of 100 historical compute +nodes, each with 16 cores, 60GB of RAM, 10 GigE Ethernet, and 1TB of +disk space. Collectively, the cluster comprised of 1600 cores, 6TB or +RAM, fast Ethernet and more than enough disk space. + +SQL statements are included in Table~\ref{tab:sql_queries} to describe the +purpose of each of the queries. Please note: +\begin{itemize} +\item The timestamp range of the queries encompassed all data. +\item Each machine was a 16-core machine with 60GB RAM and 1TB of local + disk. The machine was configured to only use 15 threads for + processing queries. +\item A memory-mapped storage engine was used (the machine was configured to memory map the data + instead of loading it into the Java heap.) +\end{itemize} + +\begin{figure} +\centering +\includegraphics[width = 3in]{cluster_scan_rate} +\caption{Druid cluster scan rate with lines indicating linear scaling + from 25 nodes.} +\label{fig:cluster_scan_rate} +\end{figure} + +\begin{figure} +\centering +\includegraphics[width = 3in]{core_scan_rate} +\caption{Druid core scan rate.} +\label{fig:core_scan_rate} +\end{figure} + +\begin{table*} + \centering + \caption{Druid Queries} + \label{tab:sql_queries} +\begin{verbatim} +1 SELECT count(*) FROM _table_ WHERE timestamp >= ? AND timestamp < ? + +2 SELECT count(*), sum(metric1) FROM _table_ WHERE timestamp >= ? AND timestamp < ? + +3 SELECT count(*), sum(metric1), sum(metric2), sum(metric3), sum(metric4) FROM _table_ + WHERE timestamp >= ? AND timestamp < ? + +4 SELECT high_card_dimension, count(*) AS cnt FROM _table_ + WHERE timestamp >= ? AND timestamp < ? + GROUP BY high_card_dimension ORDER BY cnt limit 100 + +5 SELECT high_card_dimension, count(*) AS cnt, sum(metric1) FROM _table_ + WHERE timestamp >= ? AND timestamp < ? + GROUP BY high_card_dimension ORDER BY cnt limit 100 + +6 SELECT high_card_dimension, count(*) AS cnt, sum(metric1), sum(metric2), sum(metric3), sum(metric4) + FROM _table_ WHERE timestamp >= ? AND timestamp < ? + GROUP BY high_card_dimension ORDER BY cnt limit 100 +\end{verbatim} +\end{table*} + +%\begin{enumerate} +%\item \texttt{Select high\_card\_dimension, count(*), sum(metric1), sum(metric2), sum(metric3), sum(metric4) AS cnt from \_table\_ where timestamp $\geq$ ? and timestamp $<$ ? group by high\_card\_dimension order by cnt limit 100;} +%\end{enumerate} + +Figure~\ref{fig:cluster_scan_rate} shows the cluster scan rate and +Figure~\ref{fig:core_scan_rate} shows the core scan rate. In +Figure~\ref{fig:cluster_scan_rate} we also include projected linear +scaling based on the results of the 25 core cluster. In particular, +we observe diminishing marginal returns to performance in the size of +the cluster. Under linear scaling, SQL query 1 would have achieved a +speed of 37 billion rows per second on our 75 node cluster. In fact, +the speed was 26 billion rows per second. The speed up of a parallel +computing system is often limited by the time needed for the +sequential operations of the system, in accordance with Amdahl's law +\cite{amdahl1967validity}. + +The first query listed in Table~\ref{tab:sql_queries} is a simple +count, achieving scan rates of 33M rows/second/core. We believe +the 75 node cluster was actually overprovisioned for the test +dataset, explaining the modest improvement over the 50 node cluster. +Druid's concurrency model is based on shards: one thread will scan one +shard. If the number of segments on a compute node modulo the number +of cores is small (e.g. 17 segments and 15 cores), then many of the +cores will be idle during the last round of the computation. + +When we include more aggregations we see performance degrade. This is +because of the column-oriented storage format Druid employs. For the +\texttt{count(*)} queries, Druid only has to check the timestamp column to satisfy +the ``where'' clause. As we add metrics, it has to also load those metric +values and scan over them, increasing the amount of memory scanned. + +\section{Related Work} +\label{sec:related} +Cattell \cite{cattell2011scalable} maintains a great summary about existing Scalable SQL and +NoSQL data stores. In the landscape of distributed data stores, Druid +feature-wise sits somewhere between Google’s Dremel \cite{melnik2010dremel} and Powerdrill +\cite{hall2012processing}. Druid has most of the features implemented in Dremel (Dremel +handles arbitrary nested data structures while Druid only allows for a +single level of array-based nesting) and many of the interesting +compression algorithms mentioned in Powerdrill. + +Although Druid builds on many of the same principles as other +distributed column-oriented data stores \cite{fink2012distributed}, most existing data +stores are designed to be key-value stores \cite{lerner2010redis}, or +document/extensible record stores \cite{stonebraker2005c}. Such data stores are great +solutions for traditional data warehouse needs and general +back-office/reporting usage. Typically, analysts will query these data +stores and build reports from the results. In-memory databases such as +SAP’s HANA \cite{farber2012sap} and VoltDB \cite{voltdb2010voltdb} are examples of other data stores that +are highly suited for traditional data warehousing needs. Druid is a +front-office system designed such that user-facing dashboards can be +built on top of it. Similar to \cite{paraccel2013}, Druid has analytical features +built in. The main features Druid offers over traditional data +warehousing solutions are real-time data ingestion, interactive +queries and interactive query latencies. In terms of real-time +ingestion and processing of data, Trident/Storm \cite{marz2013storm} and Streaming +Spark \cite{zaharia2012discretized} are other popular real-time computation systems, although +they lack the data storage capabilities of Druid. Spark/Shark \cite{engle2012shark} are +also doing similar work in the area of fast data analysis on large +scale data sets. Cloudera Impala \cite{cloudera2013} is another system focused on +optimizing querying performance, but more so in Hadoop environments. + +Druid leverages a unique combination of algorithms in its +architecture. Although we believe no other data store has the same set +of functionality as Druid, some of Druid’s features such as using +inverted indices to perform faster filters also exist in other data +stores \cite{macnicol2004sybase}. + +\section{Conclusions} +\label{sec:conclusions} +In this paper, we presented Druid, a distributed, column-oriented, +real-time analytical data store. Druid is a highly customizable +solution that is optimized for fast query latencies. Druid ingests +data in real-time and is fault-tolerant. We discussed the performance +of Druid on billion row data sets. We summarized key Druid architecture +aspects such as the storage format, query language and general +execution. In the future, we plan to cover more in depth the different +algorithms we’ve developed for Druid and how other systems may plug +into Druid to achieve powerful use cases. + +\section{Acknowledgements} +\label{sec:acknowledgements} +We want to thank Steve Harris and Jaypal Sethi for their feedback on improving this paper. +We also want to give recognition to Adam Smith; without him the first Metamarkets hackathon would not have +been organized and this paper would not have been created. Another special recognition to Katherine Chu for +helping to create all the images in this paper. + +Druid could not have been built without the help of many great +engineers at Metamarkets and in the community. We want to thank Danny Yuan, Jae Hyeon Bae, Paul Baclace, Dave +Nielsen, and Dhruv Parthasarathy for their +contributions to Druid. + +% The following two commands are all you need in the +% initial runs of your .tex file to +% produce the bibliography for the citations in your paper. +\bibliographystyle{abbrv} +\bibliography{druid} % druid.bib is the name of the Bibliography in this case +% You must have a proper ".bib" file +% and remember to run: +% latex bibtex latex latex +% to resolve all references + +%Generated by bibtex from your ~.bib file. Run latex, +%then bibtex, then latex twice (to resolve references). + +%APPENDIX is optional. +% ****************** APPENDIX ************************************** +% Example of an appendix; typically would start on a new page +%pagebreak + +\end{document}