From f00ffe478945e8f620e88d13e94074cddd497a81 Mon Sep 17 00:00:00 2001 From: fjy Date: Thu, 13 Mar 2014 11:33:34 -0700 Subject: [PATCH] fix broken tests --- .../main/java/io/druid/query/DataSource.java | 7 +-- .../java/io/druid/query/QueryDataSource.java | 18 ++++++-- .../java/io/druid/query/TableDataSource.java | 13 ++++-- .../post/ConstantPostAggregator.java | 2 +- publications/whitepaper/druid.pdf | Bin 594598 -> 595511 bytes publications/whitepaper/druid.tex | 41 +++++++++--------- 6 files changed, 51 insertions(+), 30 deletions(-) diff --git a/processing/src/main/java/io/druid/query/DataSource.java b/processing/src/main/java/io/druid/query/DataSource.java index 2560f2b418c..a4ef603da1f 100644 --- a/processing/src/main/java/io/druid/query/DataSource.java +++ b/processing/src/main/java/io/druid/query/DataSource.java @@ -29,9 +29,10 @@ import com.fasterxml.jackson.annotation.JsonTypeInfo; property = "type", defaultImpl = LegacyDataSource.class) @JsonSubTypes({ - @JsonSubTypes.Type(value = TableDataSource.class, name = "table"), - @JsonSubTypes.Type(value = QueryDataSource.class, name = "query") -}) + @JsonSubTypes.Type(value = TableDataSource.class, name = "table"), + @JsonSubTypes.Type(value = QueryDataSource.class, name = "query") + }) public interface DataSource { + public String getName(); } diff --git a/processing/src/main/java/io/druid/query/QueryDataSource.java b/processing/src/main/java/io/druid/query/QueryDataSource.java index 47766b32f39..3f0c397f6d4 100644 --- a/processing/src/main/java/io/druid/query/QueryDataSource.java +++ b/processing/src/main/java/io/druid/query/QueryDataSource.java @@ -37,6 +37,12 @@ public class QueryDataSource implements DataSource this.query = query; } + @Override + public String getName() + { + return query.getDataSource().getName(); + } + @JsonProperty public Query getQuery() { @@ -48,12 +54,18 @@ public class QueryDataSource implements DataSource @Override public boolean equals(Object o) { - if (this == o) return true; - if (o == null || getClass() != o.getClass()) return false; + if (this == o) { + return true; + } + if (o == null || getClass() != o.getClass()) { + return false; + } QueryDataSource that = (QueryDataSource) o; - if (!query.equals(that.query)) return false; + if (!query.equals(that.query)) { + return false; + } return true; } diff --git a/processing/src/main/java/io/druid/query/TableDataSource.java b/processing/src/main/java/io/druid/query/TableDataSource.java index 7a0d1b874be..b658454cbc1 100644 --- a/processing/src/main/java/io/druid/query/TableDataSource.java +++ b/processing/src/main/java/io/druid/query/TableDataSource.java @@ -37,6 +37,7 @@ public class TableDataSource implements DataSource } @JsonProperty + @Override public String getName() { return name; @@ -47,12 +48,18 @@ public class TableDataSource implements DataSource @Override public boolean equals(Object o) { - if (this == o) return true; - if (!(o instanceof TableDataSource)) return false; + if (this == o) { + return true; + } + if (!(o instanceof TableDataSource)) { + return false; + } TableDataSource that = (TableDataSource) o; - if (!name.equals(that.name)) return false; + if (!name.equals(that.name)) { + return false; + } return true; } diff --git a/processing/src/main/java/io/druid/query/aggregation/post/ConstantPostAggregator.java b/processing/src/main/java/io/druid/query/aggregation/post/ConstantPostAggregator.java index 338a3cfcb34..c61af6312da 100644 --- a/processing/src/main/java/io/druid/query/aggregation/post/ConstantPostAggregator.java +++ b/processing/src/main/java/io/druid/query/aggregation/post/ConstantPostAggregator.java @@ -80,7 +80,7 @@ public class ConstantPostAggregator implements PostAggregator return name; } - @JsonProperty + @JsonProperty("value") public Number getConstantValue() { return constantValue; diff --git a/publications/whitepaper/druid.pdf b/publications/whitepaper/druid.pdf index 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The complexity of the memory management is only amplified by -the fact that we are targeting highly concurrent, multi-tenant workloads. +the fact that we are targeting highly concurrent, multitenant workloads. This is, as far as the authors are aware, an active academic research problem that we would be more than willing to engage with the academic community to help resolving in a scalable manner. @@ -949,11 +949,11 @@ Druid is often used to explore data and generate reports on data. In the explore use case, the number of queries issued by a single user is much higher than in the reporting use case. Exploratory queries often involve progressively adding filters for the same time range to narrow down results. Users tend to -explore short time intervals of recent data. In the reporting use case, users -query for a much larger data interval, but already have a set of queries in -mind. +explore short time intervals of recent data. In the generate report use case, +users query for much longer data intervals, but users also already have the +queries they want to issue in mind. -\paragraph{Multitenant Workload} +\paragraph{Multitenancy} Expensive concurrent queries can be problematic in a multitenant environment. Queries for large datasources may end up hitting every historical node in a cluster and consume all cluster resources. Smaller, cheaper queries @@ -965,22 +965,23 @@ reporting use cases, and users are not expecting the same level of interactivity as when they are querying to explore data. \paragraph{Node failures} -Node failures are common in a distributed environment, but many nodes at -once failing are not. If historical nodes fail and do not recover, their -segments need to reassigned, which means we need excess cluster capacity to -load this data. The amount of additional capacity to have at any time is a -factor of cost. It is extremely rare to see more than 2 nodes fail at once and -never recover and hence, we leave enough capacity to completely reassign the -data from 2 historical nodes. +Single node failures are common in distributed environments, but many nodes +failing at once are not. If historical nodes completely fail and do not +recover, their segments need to reassigned, which means we need excess cluster +capacity to load this data. The amount of additional capacity to have at any +time contributes to the cost of running a cluster. From our experiences, it is +extremely rare to see more than 2 nodes completely fail at once and hence, we +leave enough capacity in our cluster to completely reassign the data from 2 +historical nodes. \paragraph{Data Center Outages} -Complete cluster failures are possible, but extremely rare. When running -in a single data center, it is possible for the entire data center to fail. In -such a case, a new cluster needs to be created. As long as deep storage is -available, cluster recovery time is network bound. Historical nodes need to -reload every segment from deep storage. We have experienced such a failure in -the past, and it took several hours for our entire Druid cluster to recover on -several TBs of data. +Complete cluster failures are possible, but extremely rare. If Druid is +deployed only in a single data center, it is possible for the entire data +center to fail. In such cases, new machines need to be provisioned. As long as +deep storage is still available, cluster recovery time is network bound as +historical nodes simply need to redownload every segment from deep storage. We +have experienced such failures in the past, and the recovery time was around +several hours in the AWS ecosystem on several TBs of data. \subsection{Operational Monitoring} Proper monitoring is critical to run a large scale distributed cluster.