mirror of https://github.com/apache/druid.git
finishing the paper
This commit is contained in:
parent
b4f1591260
commit
5fd506cd51
Binary file not shown.
|
@ -76,7 +76,7 @@ came to the conclusion that there was nothing in the open source world that
|
||||||
could be fully leveraged for our requirements.
|
could be fully leveraged for our requirements.
|
||||||
|
|
||||||
We ended up creating Druid, an open-source, distributed, column-oriented,
|
We ended up creating Druid, an open-source, distributed, column-oriented,
|
||||||
realtime analytical data store. In many ways, Druid shares similarities with
|
real-time analytical data store. In many ways, Druid shares similarities with
|
||||||
other OLAP systems \cite{oehler2012ibm, schrader2009oracle, lachev2005applied},
|
other OLAP systems \cite{oehler2012ibm, schrader2009oracle, lachev2005applied},
|
||||||
interactive query systems \cite{melnik2010dremel}, main-memory databases
|
interactive query systems \cite{melnik2010dremel}, main-memory databases
|
||||||
\cite{farber2012sap}, and widely-known distributed data stores
|
\cite{farber2012sap}, and widely-known distributed data stores
|
||||||
|
@ -413,7 +413,7 @@ distribution on historical nodes. The coordinator nodes tell historical nodes
|
||||||
to load new data, drop outdated data, replicate data, and move data to load
|
to load new data, drop outdated data, replicate data, and move data to load
|
||||||
balance. Druid uses a multi-version concurrency control swapping protocol for
|
balance. Druid uses a multi-version concurrency control swapping protocol for
|
||||||
managing immutable segments in order to maintain stable views. If any
|
managing immutable segments in order to maintain stable views. If any
|
||||||
immutable segment contains data that is wholly obseleted by newer segments, the
|
immutable segment contains data that is wholly obsoleted by newer segments, the
|
||||||
outdated segment is dropped from the cluster. Coordinator nodes undergo a
|
outdated segment is dropped from the cluster. Coordinator nodes undergo a
|
||||||
leader-election process that determines a single node that runs the coordinator
|
leader-election process that determines a single node that runs the coordinator
|
||||||
functionality. The remaining coordinator nodes act as redundant backups.
|
functionality. The remaining coordinator nodes act as redundant backups.
|
||||||
|
@ -780,11 +780,12 @@ involving all columns are very rare.
|
||||||
|
|
||||||
A few notes about our results:
|
A few notes about our results:
|
||||||
\begin{itemize}[leftmargin=*,beginpenalty=5000,topsep=0pt]
|
\begin{itemize}[leftmargin=*,beginpenalty=5000,topsep=0pt]
|
||||||
\item The results are from a ``hot" tier in our production cluster. We run
|
\item The results are from a ``hot" tier in our production cluster. There were
|
||||||
several tiers of varying performance in production.
|
approximately 50 data sources in the tier and several hundred users issuing
|
||||||
|
queries.
|
||||||
|
|
||||||
\item There is approximately 10.5TB of RAM available in the ``hot" tier and
|
\item There was approximately 10.5TB of RAM available in the ``hot" tier and
|
||||||
approximately 10TB of segments loaded (including replication). Collectively,
|
approximately 10TB of segments loaded. Collectively,
|
||||||
there are about 50 billion Druid rows in this tier. Results for
|
there are about 50 billion Druid rows in this tier. Results for
|
||||||
every data source are not shown.
|
every data source are not shown.
|
||||||
|
|
||||||
|
@ -799,10 +800,10 @@ Query latencies are shown in Figure~\ref{fig:query_latency} and the queries per
|
||||||
minute are shown in Figure~\ref{fig:queries_per_min}. Across all the various
|
minute are shown in Figure~\ref{fig:queries_per_min}. Across all the various
|
||||||
data sources, average query latency is approximately 550 milliseconds, with
|
data sources, average query latency is approximately 550 milliseconds, with
|
||||||
90\% of queries returning in less than 1 second, 95\% in under 2 seconds, and
|
90\% of queries returning in less than 1 second, 95\% in under 2 seconds, and
|
||||||
99\% of queries returning in less than 10 seconds.
|
99\% of queries returning in less than 10 seconds. Occasionally we observe
|
||||||
Occasionally we observe spikes in latency, as observed on February 19,
|
spikes in latency, as observed on February 19, in which case network issues on
|
||||||
in which case network issues on the Memcached instances were compounded by very high
|
the Memcached instances were compounded by very high query load on one of our
|
||||||
query load on one of our largest datasources.
|
largest datasources.
|
||||||
|
|
||||||
\begin{figure}
|
\begin{figure}
|
||||||
\centering
|
\centering
|
||||||
|
@ -881,7 +882,7 @@ ingestion setup consists of 6 nodes, totalling 360GB of RAM and 96 cores
|
||||||
(12 x Intel Xeon E5-2670).
|
(12 x Intel Xeon E5-2670).
|
||||||
|
|
||||||
Note that in this setup, several other data sources were being ingested and
|
Note that in this setup, several other data sources were being ingested and
|
||||||
many other Druid related ingestion tasks were running concurrently on those machines.
|
many other Druid related ingestion tasks were running concurrently on the machines.
|
||||||
|
|
||||||
Druid's data ingestion latency is heavily dependent on the complexity of the
|
Druid's data ingestion latency is heavily dependent on the complexity of the
|
||||||
data set being ingested. The data complexity is determined by the number of
|
data set being ingested. The data complexity is determined by the number of
|
||||||
|
@ -948,8 +949,8 @@ 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
|
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
|
adding filters for the same time range to narrow down results. Users tend to
|
||||||
explore short time intervals of recent data. In the generate report use case,
|
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
|
users query for much longer data intervals, but users also already know the
|
||||||
queries they want to issue in mind.
|
queries they want to issue.
|
||||||
|
|
||||||
\paragraph{Multitenancy}
|
\paragraph{Multitenancy}
|
||||||
Expensive concurrent queries can be problematic in a multitenant
|
Expensive concurrent queries can be problematic in a multitenant
|
||||||
|
@ -959,8 +960,8 @@ may be blocked from executing in such cases. We introduced query prioritization
|
||||||
to address these issues. Each historical node is able to prioritize which
|
to address these issues. Each historical node is able to prioritize which
|
||||||
segments it needs to scan. Proper query planning is critical for production
|
segments it needs to scan. Proper query planning is critical for production
|
||||||
workloads. Thankfully, queries for a significant amount of data tend to be for
|
workloads. Thankfully, queries for a significant amount of data tend to be for
|
||||||
reporting use cases, and users are not expecting the same level of
|
reporting use cases and can be deprioritized. Users do not expect the same level of
|
||||||
interactivity as when they are querying to explore data.
|
interactivity in this use case as when they are exploring data.
|
||||||
|
|
||||||
\paragraph{Node failures}
|
\paragraph{Node failures}
|
||||||
Single node failures are common in distributed environments, but many nodes
|
Single node failures are common in distributed environments, but many nodes
|
||||||
|
@ -979,7 +980,7 @@ 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
|
deep storage is still available, cluster recovery time is network bound as
|
||||||
historical nodes simply need to redownload every segment from deep storage. We
|
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
|
have experienced such failures in the past, and the recovery time was around
|
||||||
several hours in the AWS ecosystem on several TBs of data.
|
several hours in the AWS ecosystem for several TBs of data.
|
||||||
|
|
||||||
\subsection{Operational Monitoring}
|
\subsection{Operational Monitoring}
|
||||||
Proper monitoring is critical to run a large scale distributed cluster.
|
Proper monitoring is critical to run a large scale distributed cluster.
|
||||||
|
|
Loading…
Reference in New Issue