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new 73b3d10 [hotfix][docs] fix img links and previous docs link
73b3d10 is described below
commit 73b3d10613d95a399f60c982f94a740b4e777b3d
Author: Tony Wei <[email protected]>
AuthorDate: Tue Jun 15 11:58:12 2021 +0800
[hotfix][docs] fix img links and previous docs link
---
docs/config.toml | 1 +
.../resource-providers/standalone/overview.md | 2 +-
.../docs/dev/python/table/udfs/python_udfs.md | 2 +-
docs/content.zh/docs/dev/table/functions/udfs.md | 4 ++--
docs/content.zh/docs/dev/table/tuning.md | 6 +++---
docs/content.zh/docs/internals/job_scheduling.md | 8 ++++----
docs/content.zh/docs/libs/gelly/bipartite_graph.md | 2 +-
docs/content.zh/docs/libs/gelly/graph_api.md | 10 +++++-----
.../docs/libs/gelly/iterative_graph_processing.md | 4 ++--
docs/content.zh/docs/try-flink/datastream.md | 2 +-
docs/content/docs/dev/python/table/udfs/python_udfs.md | 2 +-
docs/content/docs/dev/table/functions/udfs.md | 4 ++--
docs/content/docs/libs/gelly/bipartite_graph.md | 2 +-
docs/content/docs/libs/gelly/graph_api.md | 10 +++++-----
.../docs/libs/gelly/iterative_graph_processing.md | 4 ++--
docs/static/fig/quickstart_cluster.png | Bin 0 -> 83790 bytes
16 files changed, 32 insertions(+), 31 deletions(-)
diff --git a/docs/config.toml b/docs/config.toml
index aeeb39a..5f21cd2 100644
--- a/docs/config.toml
+++ b/docs/config.toml
@@ -72,6 +72,7 @@ pygmentsUseClasses = true
]
PreviousDocs = [
+ ["1.13", "http://ci.apache.org/projects/flink/flink-docs-release-1.13";],
["1.12", "http://ci.apache.org/projects/flink/flink-docs-release-1.12";],
["1.11", "http://ci.apache.org/projects/flink/flink-docs-release-1.11";],
["1.10", "http://ci.apache.org/projects/flink/flink-docs-release-1.10";],
diff --git
a/docs/content.zh/docs/deployment/resource-providers/standalone/overview.md
b/docs/content.zh/docs/deployment/resource-providers/standalone/overview.md
index b446071..9718cf3 100644
--- a/docs/content.zh/docs/deployment/resource-providers/standalone/overview.md
+++ b/docs/content.zh/docs/deployment/resource-providers/standalone/overview.md
@@ -94,7 +94,7 @@ cd flink-*
<div class="row">
<div class="col-md-6 text-center">
- <img src="/page/img/quickstart_cluster.png" style="width: 60%">
+ {{<img src="/fig/quickstart_cluster.png" style="width: 60%">}}
</div>
<div class="col-md-6">
<div class="row">
diff --git a/docs/content.zh/docs/dev/python/table/udfs/python_udfs.md
b/docs/content.zh/docs/dev/python/table/udfs/python_udfs.md
index b6a5559..503783c 100644
--- a/docs/content.zh/docs/dev/python/table/udfs/python_udfs.md
+++ b/docs/content.zh/docs/dev/python/table/udfs/python_udfs.md
@@ -429,7 +429,7 @@ the function is called to compute and return the final
result.
The following example illustrates the aggregation process:
-<img alt="UDTAGG mechanism" src="/fig/udtagg-mechanism-python.png" width="80%">
+{{<img alt="UDTAGG mechanism" src="/fig/udtagg-mechanism-python.png"
width="80%">}}
In the example, we assume a table that contains data about beverages. The
table consists of three columns (`id`, `name`,
and `price`) and 5 rows. We would like to find the 2 highest prices of all
beverages in the table, i.e.,
diff --git a/docs/content.zh/docs/dev/table/functions/udfs.md
b/docs/content.zh/docs/dev/table/functions/udfs.md
index 96ee245..f9029cc 100644
--- a/docs/content.zh/docs/dev/table/functions/udfs.md
+++ b/docs/content.zh/docs/dev/table/functions/udfs.md
@@ -912,7 +912,7 @@ env.sqlQuery(
自定义聚合函数（UDAGG）是把一个表（一行或者多行，每行可以有一列或者多列）聚合成一个标量值。
-<img alt="UDAGG mechanism" src="/fig/udagg-mechanism.png" width="80%">
+{{<img alt="UDAGG mechanism" src="/fig/udagg-mechanism.png" width="80%">}}
上面的图片展示了一个聚合的例子。假设你有一个关于饮料的表。表里面有三个字段，分别是 `id`、`name`、`price`，表里有 5
行数据。假设你需要找到所有饮料里最贵的饮料的价格，即执行一个 `max()` 聚合。你需要遍历所有 5 行数据，而结果就只有一个数值。
@@ -1387,7 +1387,7 @@ t_env.sql_query("SELECT user, wAvg(points, level) AS
avgPoints FROM userScores G
自定义表值聚合函数（UDTAGG）可以把一个表（一行或者多行，每行有一列或者多列）聚合成另一张表，结果中可以有多行多列。
-<img alt="UDAGG mechanism" src="/fig/udtagg-mechanism.png" width="80%">
+{{<img alt="UDAGG mechanism" src="/fig/udtagg-mechanism.png" width="80%">}}
上图展示了一个表值聚合函数的例子。假设你有一个饮料的表，这个表有 3 列，分别是 `id`、`name` 和 `price`，一共有 5
行。假设你需要找到价格最高的两个饮料，类似于 `top2()` 表值聚合函数。你需要遍历所有 5 行数据，结果是有 2 行数据的一个表。
diff --git a/docs/content.zh/docs/dev/table/tuning.md
b/docs/content.zh/docs/dev/table/tuning.md
index 8fc9e28..0031f5b 100644
--- a/docs/content.zh/docs/dev/table/tuning.md
+++ b/docs/content.zh/docs/dev/table/tuning.md
@@ -44,7 +44,7 @@ MiniBatch 聚合的核心思想是将一组输入的数据缓存在聚合算子
下图说明了 mini-batch 聚合如何减少状态操作。
-<img src="/fig/table-streaming/minibatch_agg.png" width="50%" height="50%" />
+{{<img src="/fig/table-streaming/minibatch_agg.png" width="50%" height="50%"
>}}
默认情况下，对于无界聚合算子来说，mini-batch 优化是被禁用的。开启这项优化，需要设置选项
`table.exec.mini-batch.enabled`、`table.exec.mini-batch.allow-latency` 和
`table.exec.mini-batch.size`。更多详细信息请参见[配置]({{< ref "docs/dev/table/config"
>}}#execution-options)页面。
@@ -111,7 +111,7 @@ GROUP BY color
下图显示了 local-global 聚合如何提高性能。
-<img src="/fig/table-streaming/local_agg.png" width="70%" height="70%" />
+{{<img src="/fig/table-streaming/local_agg.png" width="70%" height="70%" >}}
@@ -193,7 +193,7 @@ GROUP BY day
下图显示了拆分 distinct 聚合如何提高性能（假设颜色表示 days，字母表示 user_id）。
-<img src="/fig/table-streaming/distinct_split.png" width="70%" height="70%" />
+{{<img src="/fig/table-streaming/distinct_split.png" width="70%" height="70%"
>}}
注意：上面是可以从这个优化中受益的最简单的示例。除此之外，Flink 还支持拆分更复杂的聚合查询，例如，多个具有不同 distinct key （例如
`COUNT(DISTINCT a), SUM(DISTINCT b)` ）的 distinct 聚合，可以与其他非 distinct 聚合（例如
`SUM`、`MAX`、`MIN`、`COUNT` ）一起使用。
diff --git a/docs/content.zh/docs/internals/job_scheduling.md
b/docs/content.zh/docs/internals/job_scheduling.md
index a059f67..1fffe5f 100644
--- a/docs/content.zh/docs/internals/job_scheduling.md
+++ b/docs/content.zh/docs/internals/job_scheduling.md
@@ -35,7 +35,7 @@ Flink 通过 _Task Slots_ 来定义执行资源。每个 TaskManager 有一到
下图很好的阐释了这一点，一个由数据源、*MapFunction* 和 *ReduceFunction* 组成的 Flink 作业，其中数据源和
MapFunction 的并行度为 4 ，ReduceFunction 的并行度为 3 。流水线由一系列的 Source - Map - Reduce
组成，运行在 2 个 TaskManager 组成的集群上，每个 TaskManager 包含 3 个 slot，整个作业的运行如下图所示。
-<img src="/fig/slots.svg" alt="Assigning Pipelines of Tasks to Slots"
height="250px" style="text-align: center;"/>
+{{<img src="/fig/slots.svg" alt="Assigning Pipelines of Tasks to Slots"
height="250px" style="text-align: center;">}}
Flink 内部通过 {{< gh_link
file="/flink-runtime/src/main/java/org/apache/flink/runtime/jobmanager/scheduler/SlotSharingGroup.java"
name="SlotSharingGroup" >}} 和 {{< gh_link
file="/flink-runtime/src/main/java/org/apache/flink/runtime/jobmanager/scheduler/CoLocationGroup.java"
name="CoLocationGroup" >}} 来定义哪些 task 可以共享一个 slot， 哪些 task 必须严格放到同一个 slot。
@@ -48,7 +48,7 @@ JobManager 会接收到一个 {{< gh_link
file="/flink-runtime/src/main/java/org
JobManager 会将 JobGraph 转换成 {{< gh_link
file="/flink-runtime/src/main/java/org/apache/flink/runtime/executiongraph/"
name="ExecutionGraph" >}}。可以将 ExecutionGraph 理解为并行版本的 JobGraph，对于每一个顶点
JobVertex，它的每个并行子 task 都有一个 {{< gh_link
file="/flink-runtime/src/main/java/org/apache/flink/runtime/executiongraph/ExecutionVertex.java"
name="ExecutionVertex" >}}。一个并行度为 100 的算子会有 1 个 JobVertex 和 100 个
ExecutionVertex。ExecutionVertex 会跟踪子 task 的执行状态。 同一个 JobVertex 的所有
ExecutionVertex 都通过 {{< gh_link fil [...]
-<img src="/fig/job_and_execution_graph.svg" alt="JobGraph and ExecutionGraph"
height="400px" style="text-align: center;"/>
+{{<img src="/fig/job_and_execution_graph.svg" alt="JobGraph and
ExecutionGraph" height="400px" style="text-align: center;">}}
每个 ExecutionGraph 都有一个与之相关的作业状态信息，用来描述当前的作业执行状态。
@@ -58,10 +58,10 @@ Flink 作业刚开始会处于 *created* 状态，然后切换到 *running* 状
*Finished*、*canceled* 和 *failed* 会导致全局的终结状态，并且触发作业的清理。跟这些状态不同，*suspended*
状态只是一个局部的终结。局部的终结意味着作业的执行已经被对应的 JobManager 终结，但是集群中另外的 JobManager
依然可以从高可用存储里获取作业信息并重启。因此一个处于 *suspended* 状态的作业不会被彻底清理掉。
-<img src="/fig/job_status.svg" alt="States and Transitions of Flink job"
height="500px" style="text-align: center;"/>
+{{<img src="/fig/job_status.svg" alt="States and Transitions of Flink job"
height="500px" style="text-align: center;">}}
在整个 ExecutionGraph 执行期间，每个并行 task 都会经历多个阶段，从 *created* 状态到 *finished* 或
*failed*。下图展示了各种状态以及他们之间的转换关系。由于一个 task 可能会被执行多次(比如在异常恢复时)，ExecutionVertex
的执行是由 {{< gh_link
file="/flink-runtime/src/main/java/org/apache/flink/runtime/executiongraph/Execution.java"
name="Execution" >}} 来跟踪的，每个 ExecutionVertex 会记录当前的执行，以及之前的执行。
-<img src="/fig/state_machine.svg" alt="States and Transitions of Task
Executions" height="300px" style="text-align: center;"/>
+{{<img src="/fig/state_machine.svg" alt="States and Transitions of Task
Executions" height="300px" style="text-align: center;">}}
{{< top >}}
diff --git a/docs/content.zh/docs/libs/gelly/bipartite_graph.md
b/docs/content.zh/docs/libs/gelly/bipartite_graph.md
index fe5c212..2aba2ef 100644
--- a/docs/content.zh/docs/libs/gelly/bipartite_graph.md
+++ b/docs/content.zh/docs/libs/gelly/bipartite_graph.md
@@ -108,7 +108,7 @@ Graph Transformations
* <strong>Projection</strong>: Projection is a common operation for bipartite
graphs that converts a bipartite graph into a regular graph. There are two
types of projections: top and bottom projections. Top projection preserves only
top nodes in the result graph and creates a link between them in a new graph
only if there is an intermediate bottom node both top nodes connect to in the
original graph. Bottom projection is the opposite to top projection, i.e. only
preserves bottom nodes an [...]
<p class="text-center">
- <img alt="Bipartite Graph Projections" width="80%"
src="/fig/bipartite_graph_projections.png"/>
+ {{<img alt="Bipartite Graph Projections" width="80%"
src="/fig/bipartite_graph_projections.png">}}
</p>
Gelly supports two sub-types of projections: simple projections and full
projections. The only difference between them is what data is associated with
edges in the result graph.
diff --git a/docs/content.zh/docs/libs/gelly/graph_api.md
b/docs/content.zh/docs/libs/gelly/graph_api.md
index 0e7fcc5..998e6b6 100644
--- a/docs/content.zh/docs/libs/gelly/graph_api.md
+++ b/docs/content.zh/docs/libs/gelly/graph_api.md
@@ -457,7 +457,7 @@ graph.subgraph((vertex => vertex.getValue > 0), (edge =>
edge.getValue < 0))
{{< /tabs >}}
<p class="text-center">
- <img alt="Filter Transformations" width="80%" src="/fig/gelly-filter.png"/>
+ {{<img alt="Filter Transformations" width="80%"
src="/fig/gelly-filter.png">}}
</p>
* <strong>Join</strong>: Gelly provides specialized methods for joining the
vertex and edge datasets with other input datasets. `joinWithVertices` joins
the vertices with a `Tuple2` input data set. The join is performed using the
vertex ID and the first field of the `Tuple2` input as the join keys. The
method returns a new `Graph` where the vertex values have been updated
according to a provided user-defined transformation function.
@@ -499,7 +499,7 @@ val networkWithWeights =
network.joinWithEdgesOnSource(vertexOutDegrees, (v1: Do
* <strong>Union</strong>: Gelly's `union()` method performs a union operation
on the vertex and edge sets of the specified graph and the current graph.
Duplicate vertices are removed from the resulting `Graph`, while if duplicate
edges exist, these will be preserved.
<p class="text-center">
- <img alt="Union Transformation" width="50%" src="/fig/gelly-union.png"/>
+ {{<img alt="Union Transformation" width="50%" src="/fig/gelly-union.png">}}
</p>
* <strong>Difference</strong>: Gelly's `difference()` method performs a
difference on the vertex and edge sets of the current graph and the specified
graph.
@@ -630,7 +630,7 @@ The neighborhood scope is defined by the `EdgeDirection`
parameter, which takes
For example, assume that you want to select the minimum weight of all
out-edges for each vertex in the following graph:
<p class="text-center">
- <img alt="reduceOnEdges Example" width="50%"
src="/fig/gelly-example-graph.png"/>
+ {{<img alt="reduceOnEdges Example" width="50%"
src="/fig/gelly-example-graph.png">}}
</p>
The following code will collect the out-edges for each vertex and apply the
`SelectMinWeight()` user-defined function on each of the resulting
neighborhoods:
@@ -669,7 +669,7 @@ final class SelectMinWeight extends
ReduceEdgesFunction[Double] {
{{< /tabs >}}
<p class="text-center">
- <img alt="reduceOnEdges Example" width="50%"
src="/fig/gelly-reduceOnEdges.png"/>
+ {{<img alt="reduceOnEdges Example" width="50%"
src="/fig/gelly-reduceOnEdges.png">}}
</p>
Similarly, assume that you would like to compute the sum of the values of all
in-coming neighbors, for every vertex. The following code will collect the
in-coming neighbors for each vertex and apply the `SumValues()` user-defined
function on each neighborhood:
@@ -708,7 +708,7 @@ final class SumValues extends ReduceNeighborsFunction[Long]
{
{{< /tabs >}}
<p class="text-center">
- <img alt="reduceOnNeighbors Example" width="70%"
src="/fig/gelly-reduceOnNeighbors.png"/>
+ {{<img alt="reduceOnNeighbors Example" width="70%"
src="/fig/gelly-reduceOnNeighbors.png">}}
</p>
When the aggregation function is not associative and commutative or when it is
desirable to return more than one values per vertex, one can use the more
general
diff --git a/docs/content.zh/docs/libs/gelly/iterative_graph_processing.md
b/docs/content.zh/docs/libs/gelly/iterative_graph_processing.md
index cf19d71..d57a43b 100644
--- a/docs/content.zh/docs/libs/gelly/iterative_graph_processing.md
+++ b/docs/content.zh/docs/libs/gelly/iterative_graph_processing.md
@@ -38,7 +38,7 @@ The computational model is shown in the figure below. The
dotted boxes correspon
In each superstep, all active vertices execute the
same user-defined computation in parallel. Supersteps are executed
synchronously, so that messages sent during one superstep are guaranteed to be
delivered in the beginning of the next superstep.
-<img alt="Vertex-Centric Computational Model" width="70%"
src="/fig/vertex-centric supersteps.png"/>
+{{<img alt="Vertex-Centric Computational Model" width="70%"
src="/fig/vertex-centric supersteps.png">}}
To use vertex-centric iterations in Gelly, the user only needs to define the
vertex compute function, `ComputeFunction`.
This function and the maximum number of iterations to run are given as
parameters to Gelly's `runVertexCentricIteration`. This method will execute the
vertex-centric iteration on the input Graph and return a new Graph, with
updated vertex values. An optional message combiner, `MessageCombiner`, can be
defined to reduce communication costs.
@@ -289,7 +289,7 @@ Additionally, the neighborhood type (in/out/all) over
which to run the scatter-
Let us consider computing Single-Source-Shortest-Paths with scatter-gather
iterations on the following graph and let vertex 1 be the source. In each
superstep, each vertex sends a candidate distance message to all its neighbors.
The message value is the sum of the current value of the vertex and the edge
weight connecting this vertex with its neighbor. Upon receiving candidate
distance messages, each vertex calculates the minimum distance and, if a
shorter path has been discovered, it up [...]
<p class="text-center">
- <img alt="Scatter-gather SSSP superstep 1" width="70%"
src="/fig/gelly-vc-sssp1.png"/>
+ {{<img alt="Scatter-gather SSSP superstep 1" width="70%"
src="/fig/gelly-vc-sssp1.png">}}
</p>
{{< tabs "d31c8b92-0d53-4e49-bb84-c68d3288dcec" >}}
diff --git a/docs/content.zh/docs/try-flink/datastream.md
b/docs/content.zh/docs/try-flink/datastream.md
index a6ee5b4..2e7b3cb 100644
--- a/docs/content.zh/docs/try-flink/datastream.md
+++ b/docs/content.zh/docs/try-flink/datastream.md
@@ -476,7 +476,7 @@ class FraudDetector extends KeyedProcessFunction[Long,
Transaction, Alert] {
假设你的欺诈检测器所处理的交易数据如下：
<p class="text-center">
- <img alt="Transactions" width="80%" src="/fig/fraud-transactions.svg"/>
+ {{<img alt="Transactions" width="80%" src="/fig/fraud-transactions.svg">}}
</p>
交易 3 和交易 4 应该被标记为欺诈行为，因为交易 3 是一个 $0.09 的小额交易，而紧随着的交易 4 是一个 $510 的大额交易。
diff --git a/docs/content/docs/dev/python/table/udfs/python_udfs.md
b/docs/content/docs/dev/python/table/udfs/python_udfs.md
index 6b9469f..cf8a3a5 100644
--- a/docs/content/docs/dev/python/table/udfs/python_udfs.md
+++ b/docs/content/docs/dev/python/table/udfs/python_udfs.md
@@ -430,7 +430,7 @@ the function is called to compute and return the final
result.
The following example illustrates the aggregation process:
-<img alt="UDTAGG mechanism" src="/fig/udtagg-mechanism-python.png" width="80%">
+{{<img alt="UDTAGG mechanism" src="/fig/udtagg-mechanism-python.png"
width="80%">}}
In the example, we assume a table that contains data about beverages. The
table consists of three columns (`id`, `name`,
and `price`) and 5 rows. We would like to find the 2 highest prices of all
beverages in the table, i.e.,
diff --git a/docs/content/docs/dev/table/functions/udfs.md
b/docs/content/docs/dev/table/functions/udfs.md
index c3feb24..80f83ee 100644
--- a/docs/content/docs/dev/table/functions/udfs.md
+++ b/docs/content/docs/dev/table/functions/udfs.md
@@ -957,7 +957,7 @@ function is called to compute and return the final result.
The following example illustrates the aggregation process:
-<img alt="UDAGG mechanism" src="/fig/udagg-mechanism.png" width="80%">
+{{<img alt="UDAGG mechanism" src="/fig/udagg-mechanism.png" width="80%">}}
In the example, we assume a table that contains data about beverages. The
table consists of three columns (`id`, `name`,
and `price`) and 5 rows. We would like to find the highest price of all
beverages in the table, i.e., perform
@@ -1294,7 +1294,7 @@ method of the function is called to compute and return
the final result.
The following example illustrates the aggregation process:
-<img alt="UDTAGG mechanism" src="/fig/udtagg-mechanism.png" width="80%">
+{{<img alt="UDTAGG mechanism" src="/fig/udtagg-mechanism.png" width="80%">}}
In the example, we assume a table that contains data about beverages. The
table consists of three columns (`id`, `name`,
and `price`) and 5 rows. We would like to find the 2 highest prices of all
beverages in the table, i.e.,
diff --git a/docs/content/docs/libs/gelly/bipartite_graph.md
b/docs/content/docs/libs/gelly/bipartite_graph.md
index 509b653..aefc527 100644
--- a/docs/content/docs/libs/gelly/bipartite_graph.md
+++ b/docs/content/docs/libs/gelly/bipartite_graph.md
@@ -108,7 +108,7 @@ Graph Transformations
* <strong>Projection</strong>: Projection is a common operation for bipartite
graphs that converts a bipartite graph into a regular graph. There are two
types of projections: top and bottom projections. Top projection preserves only
top nodes in the result graph and creates a link between them in a new graph
only if there is an intermediate bottom node both top nodes connect to in the
original graph. Bottom projection is the opposite to top projection, i.e. only
preserves bottom nodes an [...]
<p class="text-center">
- <img alt="Bipartite Graph Projections" width="80%"
src="/fig/bipartite_graph_projections.png"/>
+ {{<img alt="Bipartite Graph Projections" width="80%"
src="/fig/bipartite_graph_projections.png">}}
</p>
Gelly supports two sub-types of projections: simple projections and full
projections. The only difference between them is what data is associated with
edges in the result graph.
diff --git a/docs/content/docs/libs/gelly/graph_api.md
b/docs/content/docs/libs/gelly/graph_api.md
index db75e70..a488a56 100644
--- a/docs/content/docs/libs/gelly/graph_api.md
+++ b/docs/content/docs/libs/gelly/graph_api.md
@@ -457,7 +457,7 @@ graph.subgraph((vertex => vertex.getValue > 0), (edge =>
edge.getValue < 0))
{{< /tabs >}}
<p class="text-center">
- <img alt="Filter Transformations" width="80%" src="/fig/gelly-filter.png"/>
+ {{<img alt="Filter Transformations" width="80%"
src="/fig/gelly-filter.png">}}
</p>
* <strong>Join</strong>: Gelly provides specialized methods for joining the
vertex and edge datasets with other input datasets. `joinWithVertices` joins
the vertices with a `Tuple2` input data set. The join is performed using the
vertex ID and the first field of the `Tuple2` input as the join keys. The
method returns a new `Graph` where the vertex values have been updated
according to a provided user-defined transformation function.
@@ -499,7 +499,7 @@ val networkWithWeights =
network.joinWithEdgesOnSource(vertexOutDegrees, (v1: Do
* <strong>Union</strong>: Gelly's `union()` method performs a union operation
on the vertex and edge sets of the specified graph and the current graph.
Duplicate vertices are removed from the resulting `Graph`, while if duplicate
edges exist, these will be preserved.
<p class="text-center">
- <img alt="Union Transformation" width="50%" src="/fig/gelly-union.png"/>
+ {{<img alt="Union Transformation" width="50%" src="/fig/gelly-union.png">}}
</p>
* <strong>Difference</strong>: Gelly's `difference()` method performs a
difference on the vertex and edge sets of the current graph and the specified
graph.
@@ -630,7 +630,7 @@ The neighborhood scope is defined by the `EdgeDirection`
parameter, which takes
For example, assume that you want to select the minimum weight of all
out-edges for each vertex in the following graph:
<p class="text-center">
- <img alt="reduceOnEdges Example" width="50%"
src="/fig/gelly-example-graph.png"/>
+ {{<img alt="reduceOnEdges Example" width="50%"
src="/fig/gelly-example-graph.png">}}
</p>
The following code will collect the out-edges for each vertex and apply the
`SelectMinWeight()` user-defined function on each of the resulting
neighborhoods:
@@ -669,7 +669,7 @@ final class SelectMinWeight extends
ReduceEdgesFunction[Double] {
{{< /tabs >}}
<p class="text-center">
- <img alt="reduceOnEdges Example" width="50%"
src="/fig/gelly-reduceOnEdges.png"/>
+ {{<img alt="reduceOnEdges Example" width="50%"
src="/fig/gelly-reduceOnEdges.png">}}
</p>
Similarly, assume that you would like to compute the sum of the values of all
in-coming neighbors, for every vertex. The following code will collect the
in-coming neighbors for each vertex and apply the `SumValues()` user-defined
function on each neighborhood:
@@ -708,7 +708,7 @@ final class SumValues extends ReduceNeighborsFunction[Long]
{
{{< /tabs >}}
<p class="text-center">
- <img alt="reduceOnNeighbors Example" width="70%"
src="/fig/gelly-reduceOnNeighbors.png"/>
+ {{<img alt="reduceOnNeighbors Example" width="70%"
src="/fig/gelly-reduceOnNeighbors.png">}}
</p>
When the aggregation function is not associative and commutative or when it is
desirable to return more than one values per vertex, one can use the more
general
diff --git a/docs/content/docs/libs/gelly/iterative_graph_processing.md
b/docs/content/docs/libs/gelly/iterative_graph_processing.md
index f8e74eb..7d5ede6 100644
--- a/docs/content/docs/libs/gelly/iterative_graph_processing.md
+++ b/docs/content/docs/libs/gelly/iterative_graph_processing.md
@@ -38,7 +38,7 @@ The computational model is shown in the figure below. The
dotted boxes correspon
In each superstep, all active vertices execute the
same user-defined computation in parallel. Supersteps are executed
synchronously, so that messages sent during one superstep are guaranteed to be
delivered in the beginning of the next superstep.
-<img alt="Vertex-Centric Computational Model" width="70%"
src="/fig/vertex-centric supersteps.png"/>
+{{<img alt="Vertex-Centric Computational Model" width="70%"
src="/fig/vertex-centric supersteps.png">}}
To use vertex-centric iterations in Gelly, the user only needs to define the
vertex compute function, `ComputeFunction`.
This function and the maximum number of iterations to run are given as
parameters to Gelly's `runVertexCentricIteration`. This method will execute the
vertex-centric iteration on the input Graph and return a new Graph, with
updated vertex values. An optional message combiner, `MessageCombiner`, can be
defined to reduce communication costs.
@@ -289,7 +289,7 @@ Additionally, the neighborhood type (in/out/all) over
which to run the scatter-
Let us consider computing Single-Source-Shortest-Paths with scatter-gather
iterations on the following graph and let vertex 1 be the source. In each
superstep, each vertex sends a candidate distance message to all its neighbors.
The message value is the sum of the current value of the vertex and the edge
weight connecting this vertex with its neighbor. Upon receiving candidate
distance messages, each vertex calculates the minimum distance and, if a
shorter path has been discovered, it up [...]
<p class="text-center">
- <img alt="Scatter-gather SSSP superstep 1" width="70%"
src="/fig/gelly-vc-sssp1.png"/>
+ {{<img alt="Scatter-gather SSSP superstep 1" width="70%"
src="/fig/gelly-vc-sssp1.png">}}
</p>
{{< tabs "d31c8b92-0d53-4e49-bb84-c68d3288dcec" >}}
diff --git a/docs/static/fig/quickstart_cluster.png
b/docs/static/fig/quickstart_cluster.png
new file mode 100644
index 0000000..9e24795
Binary files /dev/null and b/docs/static/fig/quickstart_cluster.png differ
