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b/.devcontainer/devcontainer.json new file mode 100644 index 000000000..9a995a9ad --- /dev/null +++ b/.devcontainer/devcontainer.json @@ -0,0 +1,88 @@ +/* + * Copyright 2022-2023 Alibaba Group Holding Limited. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + + +// For format details, see https://aka.ms/devcontainer.json. For config options, see the +// README at: https://github.com/devcontainers/templates/tree/main/src/javascript-node +{ + "name": "GraphAr", + // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile + "image": "registry.cn-hongkong.aliyuncs.com/graphscope/graphar-dev:latest", + // "image": "ubuntu:22.04", + + // Features to add to the dev container. More info: https://containers.dev/features. + "features": { + "ghcr.io/devcontainers/features/common-utils:2":{ + "installZsh": "true", + "configureZshAsDefaultShell": "true", + "installOhMyZsh": true, + "upgradePackages": "false" + } + }, + // Configure tool-specific properties. + "customizations": { + // Configure properties specific to VS Code. + "vscode": { + "settings": {}, + "extensions": [ + "streetsidesoftware.code-spell-checker", + "eamodio.gitlens", + "github.copilot", + "github.copilot-labs" + ] + } + }, + + // Set `remoteUser` to `root` to connect as root instead. More info: https://aka.ms/vscode-remote/containers/non-root. + "remoteUser": "graphar", + + // Use 'postCreateCommand' to run commands before the container is created. + "initializeCommand": "sudo docker pull registry.cn-hongkong.aliyuncs.com/graphscope/graphar-dev:latest", + + // Uncomment this to enable C++ and Rust debugging in containers + // "capAdd": ["SYS_PTRACE"], + // "securityOpt": ["seccomp=unconfined"], + + // Use 'forwardPorts' to make a list of ports inside the container available locally. + // "forwardPorts": [3000], + + // Use 'portsAttributes' to set default properties for specific forwarded ports. + // More info: https://containers.dev/implementors/json_reference/#port-attributes + // "portsAttributes": { + // "9000": { + // "label": "Hello Remote World", + // "onAutoForward": "notify" + // } + // }, + + // Use 'postCreateCommand' to run commands after the container is created. + // "postCreateCommand": "yarn install" + + // Improve performance + + // Uncomment these to mount a folder to a volume + // https://code.visualstudio.com/remote/advancedcontainers/improve-performance#_use-a-targeted-named-volume + // "mounts": [ + // "source=${localWorkspaceFolderBasename}-node_modules,target=${containerWorkspaceFolder}/node_modules,type=volume" + // ], + + + // Uncomment these to use a named volume for your entire source tree + // https://code.visualstudio.com/remote/advancedcontainers/improve-performance#_use-a-named-volume-for-your-entire-source-tree + // "workspaceMount": "source=gs,target=/workspaces,type=volume", + // "workspaceFolder": "/workspaces" + "postCreateCommand": "sudo chown -R graphar /workspaces && bash pre-commit/install-hook.sh && bash pre-commit/prepare-commit-msg" +} diff --git a/.gitleaks.toml b/.gitleaks.toml new file mode 100644 index 000000000..4451dfaa2 --- /dev/null +++ b/.gitleaks.toml @@ -0,0 +1,83 @@ +title = "Gitleaks for Vineyard" + +[extend] +useDefault = true + +[[rules]] +description = "Alibaba AccessKey ID" +id = "alibaba-access-key-id" +regex = '''(?i)((LTAI)[a-z0-9]+)''' +keywords = [ + "ltai", +] + +[[rules]] +description = "Alibaba AccessKey ID" +id = "alibaba-access-id-in-config" +regex = '''(?i)((access).?id\s*=\s*.+)''' +keywords = [ + "access", +] + +[[rules]] +description = "Alibaba AccessKey ID" +id = "alibaba-access-key-in-config" +regex = '''(?i)((access).?key\s*=\s*.+)''' +keywords = [ + "access", +] + +[[rules]] +description = "Alibaba AccessKey ID" +id = "alibaba-access-secret-in-config" +regex = '''(?i)((access).?secret\s*=\s*.+)''' +keywords = [ + "access", + "secret", +] + +[[rules]] +description = "Alibaba AccessKey ID" +id = "alibaba-access-key-id-in-config" +regex = '''(?i)((access).?key.?id\s*=\s*.+)''' +keywords = [ + "access", +] + +[rules.allowlist] +paths = [ + '''modules/io/python/drivers/io/tests/test_open.py''', + '''modules/io/python/drivers/io/tests/test_serialize.py''', +] + +[[rules]] +description = "Alibaba AccessKey ID" +id = "alibaba-access-key-secret-in-config" +regex = '''(?i)((access).?key.?secret\s*=\s*.+)''' +keywords = [ + "access", + "secret", +] + +[rules.allowlist] +paths = [ + '''modules/io/python/drivers/io/tests/test_open.py''', + '''modules/io/python/drivers/io/tests/test_serialize.py''', +] + +[[rules]] +description = "Alibaba AccessKey ID" +id = "alibaba-secret-access-key-in-config" +regex = '''(?i)((secret).?access.?key\s*=\s*.+)''' +keywords = [ + "access", + "secret", +] + +[allowlist] +paths = [ + '''build''', + '''docs/_build''', + '''docs/_templates/footer.html''', + '''thirdparty''', +] diff --git a/.licenserc.yaml b/.licenserc.yaml new file mode 100644 index 000000000..c6234a1f0 --- /dev/null +++ b/.licenserc.yaml @@ -0,0 +1,51 @@ +header: + license: + spdx-id: Apache-2.0 + copyright-owner: Apache Software Foundation + content: | + Copyright 2022-2023 Alibaba Group Holding Limited. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + + paths-ignore: + - 'dist' + - 'licenses' + - '**/*.md' + - 'LICENSE' + - 'NOTICE' + - 'testing' + - 'spark/src/test/resources' + - 'java/src/test/resources' + - '.licenserc.yaml' + - '.gitignore' + - '.gitleaks.toml' + - '.gitmodules' + - 'pre-commit-config.yaml' + - 'docs' + - '**/.gitignore' + - 'spark/.scalafix.conf' + - 'spark/.scalafmt.conf' + - 'cpp/apidoc' + - 'spark/src/main/scala/com/alibaba/graphar/datasources' + - '*.md' + - '*.rst' + - '**/*.json' + - 'pyspark/poetry.lock' # This file is generated automatically by Poetry-tool; there is no way to add license header + + comment: on-failure + +# If you don't want to check dependencies' license compatibility, remove the following part +dependency: + files: + - spark/pom.xml # If this is a maven project. + - java/pom.xml # If this is a maven project. diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 000000000..e69de29bb diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 000000000..338f09c93 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,22 @@ +# Copyright 2022-2023 Alibaba Group Holding Limited. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +repos: + - repo: https://github.com/zricethezav/gitleaks + rev: v8.15.0 + hooks: + - id: gitleaks + args: + - '--verbose' + diff --git a/_images/edge_logical_table.png b/_images/edge_logical_table.png new file mode 100644 index 0000000000000000000000000000000000000000..4ac9b001b043b55799c323cf8f343cd05b6491cd GIT binary patch literal 144242 zcmeFZcUY6#_AW{nK@deiL5c;CE={Bdk=|55dXV0G@6Cqv-U%Xv4$>h3f`#5|=t1d( z5a~UfkF}O-@83CluYd2o&pkX3Bv1GVXA$`&*`x&RxBrkv3)5-mVzN0KULm+CI;CTHnXs-rp7}Oi}ALb z-+s)`f5R3Y$U>_8f=d}x;}$4m@!Zl^K0Nf^!@L(X5m8ofaUBhbd@aC)bKcg=cIT<`1E+hz%zW{R|=ou@8|OP5AX>8 zc%1+b@0|@E(O=i71CJL!$-wW$d;WYR$RYTB1xb1i;qT`JKQAu5`+Q;!c)0AWsON@< zM|b_=7vBmp3@j)JPwA-hJZpX3vCO*fiD-@E70t({RuQ+BxA-Z&2+u}YiGnw=J?hu5F@Ni$DO z+F(xttvo%QvVBnX$piQA+zG}bAZ7Wlf2clzFgKA_9z;|iJpBLuhZ~Hu-&sL99K4SnJ^7~zQLzku7yYky^~XP`Qt(2c4HFZj 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b/_sources/cpp/examples/bgl.rst.txt @@ -0,0 +1,93 @@ +Co-Work with BGL +============================ + +The `Boost Graph Library (BGL) `_ is the first C++ library to apply the principles of generic programming to the construction of the advanced data structures and algorithms used in graph computations. The BGL graph interface and graph components are generic in the same sense as the Standard Template Library (STL). And it provides some built-in algorithms which cover a core set of algorithm patterns and a larger set of graph algorithms. + +We take calculating CC as an example, to demonstrate how BGL works with GraphAr. A weakly connected component is a maximal subgraph of a graph such that for every pair of vertices in it, there is an undirected path connecting them. And the CC algorithm is to identify all such components in a graph. Learn more about `the CC algorithm `_. + +The source code of CC based on BGL can be found at `bgl_example.cc`_. In this program, the graph information file is first read to get the metadata: + +.. code:: C++ + + std::string path = ... // the path of the graph information file + auto graph_info = GraphArchive::GraphInfo::Load(path).value(); + +And then, the vertex collection and the edge collection are established as the handles to access the graph data: + +.. code:: C++ + + auto maybe_vertices = GraphArchive::VerticesCollection::Make(graph_info, "person"); + auto vertices = maybe_vertices.value(); + auto maybe_edges = GraphArchive::EdgesCollection::Make(graph_info, "person", "knows", "person", GraphArchive::AdjListType::ordered_by_source); + auto edges = maybe_edges.value(); + +Next, we construct the in-memory graph data structure for BGL by traversing the vertices and edges via GraphAr's high-level reading interface (the vertex iterator and the edge iterator): + +.. code:: C++ + + // define the Graph type in BGL + typedef boost::adjacency_list, // vertex property + boost::no_property> Graph; // no edge property + // descriptors for vertex in BGL + typedef typename boost::graph_traits::vertex_descriptor Vertex; + + // declare a graph object with (num_vertices) vertices and an edge iterator + std::vector> edges_array; + auto it_begin = edges->begin(), it_end = edges->end(); + for (auto it = it_begin; it != it_end; ++it) + edges_array.push_back(std::make_pair(it.source(), it.destination())); + Graph g(edges_array.begin(), edges_array.end(), num_vertices); + + // define the internal vertex property "id" + boost::property_map::type id = get(boost::vertex_name_t(), g); + auto v_it_begin = vertices->begin(), v_it_end = vertices->end(); + for (auto it = v_it_begin; it != v_it_end; ++it) { + auto vertex = *it; + boost::put(id, vertex.id(), vertex.property("id").value()); + } + +After that, an internal CC algorithm provided by BGL is called: + +.. code:: C++ + + // define the external vertex property "component" + std::vector component(num_vertices); + // call algorithm: cc + int cc_num = boost::connected_components(g, &component[0]); + std::cout << "Total number of components: " << cc_num << std::endl; + +Finally, we could use a **VerticesBuilder** of GraphAr to write the results to new generated GAR files: + +.. code:: C++ + + // construct a new property group + GraphArchive::Property cc = {"cc", GraphArchive::int32(), false}; + std::vector property_vector = {cc}; + auto group = GraphArchive::CreatePropertyGroup(property_vector, GraphArchive::FileType::PARQUET); + + // construct the new vertex info + std::string vertex_label = "cc_result", vertex_prefix = "result/"; + int chunk_size = 100; + auto new_info = GraphArchive::CreateVertexInfo(vertex_label, chunk_size, {group}, vertex_prefix); + + // access the vertices via the index map and vertex iterator of BGL + typedef boost::property_map::type IndexMap; + IndexMap index = boost::get(boost::vertex_index, g); + typedef boost::graph_traits::vertex_iterator vertex_iter; + std::pair vp; + + // dump the results through the VerticesBuilder + GraphArchive::builder::VerticesBuilder builder(new_info, "/tmp/"); + for (vp = boost::vertices(g); vp.first!= vp.second; ++vp.first) { + Vertex v = *vp.first; + GraphArchive::builder::Vertex vertex(index[v]); + vertex.AddProperty(cc.name, component[index[v]]); + builder.AddVertex(vertex); + } + builder.Dump(); + + +.. _bgl_example.cc: https://github.com/alibaba/GraphAr/blob/main/cpp/examples/bgl_example.cc diff --git a/_sources/cpp/examples/graphscope.rst.txt b/_sources/cpp/examples/graphscope.rst.txt new file mode 100644 index 000000000..43f195c6b --- /dev/null +++ b/_sources/cpp/examples/graphscope.rst.txt @@ -0,0 +1,32 @@ +Integrate into GraphScope +============================ + +`GraphScope `_ is a unified distributed graph computing platform that provides a one-stop environment for performing diverse graph operations on a cluster through a user-friendly Python interface. As an important application case of GraphAr, we have integrated it into GraphScope. + +GraphScope works on a graph G fragmented via a partition strategy picked by the user and each worker maintains a fragment of G. Given a query, it posts the same query to all the workers and computes following the BSP (Bulk Synchronous Parallel) model. More specifically, each worker first executes processing against its local fragment, to compute partial answers in parallel. And then each worker may exchange partial results with other processors via synchronous message passing. + +To integrate GraphAr into GraphScope, we implemented *ArrowFragmentBuilder* and *ArrowFragmentWriter*. *ArrowFragmentBuilder* establishes the fragments for workers of GraphScope through reading GAR files in parallel. Conversely, *ArrowFragmentWriter* can take the GraphScope fragments and save them as GAR files. If you're interested in knowing more about the implementation, please refer to the `source code `_. + + +Performance Report +------------------------ + +Parameter settings +`````````````````` +The time performance of *ArrowFragmentBuilder* and *ArrowFragmentWriter* in GraphScope is heavily dependent on the partitioning of the graph into GAR files, that is, the *vertex chunk size* and *edge chunk size*, which are specified in the vertex information file and in the edge information file, respectively. See `GraphAr File Format <../user-guide/file-format.html>`_ to understand the chunk size definitions in GAR. + +Generally speaking, fewer chunks are created if the file size is large. On small graphs, this can be disadvantageous as it reduces the degree of parallelism, prolonging disk I/O time. On the other hand, having too many small files increases the overhead associated with the file system and the file parser. + +We have conducted micro benchmarks to compare the time performance for reading/writing GAR files by *ArrowFragmentBuilder*/*ArrowFragmentWriter*, across different *vertex chunk size* and *edge chunk size* configurations. The settings we recommend for *vertex chunk size* and *edge chunk size* are **2^18** and **2^22**, respectively, which lead to efficient performance in most cases. These settings can be used as the reference values when integrating GraphAr into other systems besides GraphScope. + +Time performance results +```````````````````````` +Here we report the performance results of *ArrowFragmentBuilder*, and compare it with loading the same graph through the default loading strategy of GraphScope (through reading the csv files in parallel) . The execution time reported below includes loading the graph data from the disk into memory, as well as building GraphScope fragments from such data. The experiments are conducted on a cluster of 4 AliCloud ecs.r6.6xlarge instances (24vCPU, 192GB memory), and using `com-friendster `_ (a simple graph) and `ldbc-snb-30 `_ (a multi-labeled property graph) as datasets. + ++----------------+---------+-----------------+-----------------+ +| Dataset | Workers | Default Loading | GraphAr Loading | ++================+=========+=================+=================+ +| com-friendster | 4 | 282s | 54s | ++----------------+---------+-----------------+-----------------+ +| ldbc-snb-30 | 4 | 196s | 40s | ++----------------+---------+-----------------+-----------------+ diff --git a/_sources/cpp/examples/index.rst.txt b/_sources/cpp/examples/index.rst.txt new file mode 100644 index 000000000..5ba1b3576 --- /dev/null +++ b/_sources/cpp/examples/index.rst.txt @@ -0,0 +1,9 @@ +Examples +--------- + +.. toctree:: + :maxdepth: 2 + + bgl + graphscope + out-of-core diff --git a/_sources/cpp/examples/out-of-core.rst.txt b/_sources/cpp/examples/out-of-core.rst.txt new file mode 100644 index 000000000..062963dc5 --- /dev/null +++ b/_sources/cpp/examples/out-of-core.rst.txt @@ -0,0 +1,122 @@ +Out-of-core Graph Algorithms +============================ + +An important application case of GraphAr is to serve out-of-core graph processing scenarios. With the graph data saved as GAR files in the disk, GraphAr provides a set of reading interfaces to allow to load part of graph data into memory when needed, to conduct analytics. While it is more convenient and efficient to store the entirety of the graph in memory (as is done in BGL), out-of-core graph processing makes it possible to complete analytics on the large-scale graphs using limited memory/computing resources. + +The are some out-of-core graph analytic algorithms that have been implemented based on GraphAr, include: + +- PageRank (PR) +- Connected Components (CC) +- Breadth First Search (BFS) + +These algorithms represent for different compute patterns and are usually building blocks for constructing other graph algorithms. + +PageRank +------------------------ + +`PageRank (PR) `_ is an algorithm used by Google Search to rank web pages in their search engine results. The source code of PageRank based on GraphAr located at `pagerank_example.cc`_, and the explanations can be found in the `Getting Started <../user-guide/getting-started.html#a-pagerank-example>`_ page. + +Connected Components +------------------------ + +A weakly connected component is a maximal subgraph of a graph such that for every pair of vertices in it, there is an undirected path connecting them. And `Connected Components (CC) `_ is an algorithm to identify all weakly connected components in a graph. `CC based on BGL `_ is provided in GraphAr, also, we implement out-of-core algorithms for this application. + +A typical method for calculating CC is label propagation. In this algorithm, each vertex is attached with a property which represents its component label, being its own vertex id initially. In the subsequent supersteps (i.e., iterations), a vertex will update its label if it receives a smaller id and then it propagates this id to all its neighbors. + +This algorithm can be implemented based on streaming the edges via GraphAr's reading interface. That is to say, the edges are accessed and processed chunk by chunk, instead of being loaded into memory at once (as in the BGL example). + +.. code:: C++ + + // construct the edge collection in GraphAr + auto edges = ... + auto it_begin = edges->begin(), it_end = edges->end(); + + // initialize for all vertices + std::vector component(num_vertices); + for (GraphArchive::IdType i = 0; i < num_vertices; i++) + component[i] = i; + + // stream all edges for each iteration + for (int iter = 0; ; iter++) { + bool flag = false; + for (auto it = it_begin; it != it_end; ++it) { + GraphArchive::IdType src = it.source(), dst = it.destination(); + // update + if (component[src] < component[dst]) { + component[dst] = component[src]; + flag = true; + } else if (component[src] > component[dst]) { + component[src] = component[dst]; + flag = true; + } + } + // check if it should terminate + if (!flag) break; + } + +The file `cc_stream_example.cc`_ located inside the source tree contains the complete implementation for this algorithm. Also, we can only process active vertices (the vertices which are updated in the last iteration) and the corresponding edges for each iteration, since an inactive vertex does not need to update its neighbors. Please refer to `cc_push_example.cc`_ for the complete code. + +.. tip:: + + In this example, two kinds of edges are used. The **ordered_by_source** edges are used to access all outgoing edges of an active vertex, and **ordered_by_dest** edges are used to access the incoming edges. In this way, all the neighbors of an active vertex can be accessed and processed. + + Although GraphAr supports to get the outgoing (incoming) edges of a single vertex for all adjList types, it is most efficient when the type is **ordered_by_source** (**ordered_by_dest**) since it can avoid to read redundant data. + +Breadth First Search +------------------------ + +`Breadth First Search (BFS) `_ is a traversing algorithm that starts from a selected vertex (the root) and traverse the graph layerwise thus exploring the neighbor vertices (vertices which are directly connected to the root), and then it moves towards the next-level neighbor vertices. + +An out-of-core BFS algorithm could be implemented based on streaming the graph data via GraphAr. For each vertex, a property named *distance* is created and initialized to represent the distance from the root to this vertex. As with the standard BFS algorithms in other graph processing systems, for the iteration/superstep *i* (starting from 0), the active vertices contain all vertices reachable from the root in *i* hops (i.e., *distance[v]= i*). At the beginning of the algorithm, only the root vertex is active. This algorithm terminates when there are no more active vertices. + +.. code:: C++ + + // construct the edge collection in GraphAr + auto edges = ... + auto it_begin = edges->begin(), it_end = edges->end(); + + // initialize for all vertices + GraphArchive::IdType root = 0; // the BFS root + std::vector distance(num_vertices); + for (GraphArchive::IdType i = 0; i < num_vertices; i++) + distance[i] = (i == root ? 0 : -1); + + // stream all edges for each iteration + for (int iter = 0; ; iter++) { + GraphArchive::IdType count = 0; + for (auto it = it_begin; it != it_end; ++it) { + GraphArchive::IdType src = it.source(), dst = it.destination(); + // update + if (distance[src] == iter && distance[dst] == -1) { + distance[dst] = distance[src] + 1; + count++; + } + } + // check if it should terminate + if (count == 0) break; + } + +The above algorithm is implemented based on streaming all edges for each iteration, the source code can be found at `bfs_stream_example.cc`_. + +Meanwhile, BFS could be implemented in a **push**-style which only traverses the edges that from active vertices for each iteration, which is typically more efficient on real-world graphs. This implementation can be found at `bfs_push_example.cc`_. Similarly, we provide a BFS implementation in a **pull**-style which only traverses the edges that lead to non-visited vertices (i.e., the vertices that have not been traversed), as shown in `bfs_pull_example.cc`_. + +.. tip:: + + In common cases of graph processing, the **push**-style is more efficient when the set of active vertices is very sparse, while the **pull**-style fits when it is dense. + +In some cases, it is required to record the path of BFS, that is, to maintain each vertex's predecessor (also called *father*) in the traversing tree rather than only recording the distance. The implementation of BFS with recording fathers can be found at `bfs_father_example.cc`_. + + +.. _pagerank_example.cc: https://github.com/alibaba/GraphAr/blob/main/cpp/examples/pagerank_example.cc + +.. _cc_stream_example.cc: https://github.com/alibaba/GraphAr/blob/main/cpp/examples/cc_stream_example.cc + +.. _cc_push_example.cc: https://github.com/alibaba/GraphAr/blob/main/cpp/examples/cc_push_example.cc + +.. _bfs_stream_example.cc: https://github.com/alibaba/GraphAr/blob/main/cpp/examples/bfs_stream_example.cc + +.. _bfs_push_example.cc: https://github.com/alibaba/GraphAr/blob/main/cpp/examples/bfs_push_example.cc + +.. _bfs_pull_example.cc: https://github.com/alibaba/GraphAr/blob/main/cpp/examples/bfs_pull_example.cc + +.. _bfs_father_example.cc: https://github.com/alibaba/GraphAr/blob/main/cpp/examples/bfs_father_example.cc diff --git a/_sources/cpp/getting-started.rst.txt b/_sources/cpp/getting-started.rst.txt new file mode 100644 index 000000000..2989e5d29 --- /dev/null +++ b/_sources/cpp/getting-started.rst.txt @@ -0,0 +1,198 @@ +Getting Started +============================ + +This article is a quick guide that explains how to work with GraphAr C++. To begin with, please refer to the `Building Steps`_ to install GraphAr. + + +GAR Information Files +------------------------ + +GAR uses a group of Yaml files to save the meta information for a graph. + +Graph information +````````````````` +The graph information file defines the most basic information of a graph includes its name, the root directory path of the data files, the vertex information and edge information files it contains, and the version of GraphAr. For example, the file "ldbc_sample.graph.yml" defines an example graph named "ldbc_sample", which includes one type of vertices ("person") and one type of edges ("person knows person"). + +.. code:: Yaml + + name: ldbc_sample + prefix: ./ + vertices: + - person.vertex.yml + edges: + - person_knows_person.edge.yml + version: 1 + +Vertex information +`````````````````` +Each vertex information file defines a single group of vertices with the same vertex label, e.g., "person" in this case. The vertex chunk size, the relative path for vertex data files and the version of GraphAr are specified. These vertices could have some properties, which are divided into property groups. Each property group has its own file type (CSV, ORC or Parquet) and the prefix of the relative path for its data files, it also lists all properties in this group, with every property contains the name, data type and if it is the primary key. + +The file `person.vertex.yml`_ located inside the test data contains an example of the vertex information file. In this example, the "person" vertices have two property groups. The first group contains only one property (named "id") and the second group contains three properties ("firstName", "lastName" and "gender"). + +Edge information +```````````````` +Each edge information file defines a single type of edges with specific labels for the source vertex, destination vertex and the edge, e.g., "person_knows_person" in this case. It defines the meta information such as the edge chunk size, the source vertex chunk size, the destination vertex chunk size, if the edges are directed or not, the relative file path for edge data files, the adjLists and the version of GraphAr. The file `person_knows_person.edge.yml`_ located inside the test data contains an example of the edge information file. + +In GAR format, separate data files are used to store the structure (called adjList) and the properties for edges. The adjList type can be either of **unordered_by_source**, **unordered_by_dest**, **ordered_by_source** or **ordered_by_dest** (see `Edges in GraphAr `_ for more). For a specific type of adjList, the meta information includes its file path prefix, the file type, as well as all the property groups attached. + +.. note:: + + It is allowed to store different types of adjLists for a group of edges at the same time. + + + +GAR Data Files +------------------------ + +Property data +````````````` +The vertex properties are stored in vertex property chunks with the chunk size specified by the vertex information file. Different property groups correspond to individual groups of data files. +In our example, the property group ("first name", "last name", "gender") for vertex chunk 0 of "person" vertices are stored in `./vertex/person/firstName_lastName_gender/chunk0`_. + +In practice of graph processing, it is common to only query a subset of columns of the properties. Thus, the column-oriented formats like Apache ORC and Apache Parquet are more efficient, which eliminate the need to read columns that are not relevant. We also provide data files in ORC and Parquet for the example graph in the `test data`_. + +Similar with vertices, the edge properties are stored in edge property chunks. For each vertex chunk, its associated edges (if the edge type is **ordered_by_source** or **unordered_by_source**, the associated edges are those in which the source vertex is in that chunk; otherwise, if the edge type is **ordered_by_dest** or **unordered_by_dest**, the associated edges are those in which the destination is in that chunk) are maintained in some edge chunks, with the size of each chunk not exceeding the edge chunk size specified in the edge information file. + +For instance, the file `./edge/person_knows_person/ordered_by_source/creationDate/part0/chunk0`_ stores the property group "creationDate" of "person_knows_person" edges for the first edge chunk of the first vertex chunk, and the adjList type of the edges is **ordered_by_source**. + +AdjList data +```````````` +The adjList in GAR describes the topology structure, i.e., the internal id of the source vertex and the destination vertex for each of a group of edges. As explained in `Edges in GraphAr `_, the edges are separated into edge chunks, and each edge chunk has its own adjList table and 0 or more property tables. + +For example, the file `./edge/person_knows_person/ordered_by_source/adj_list/part0/chunk0`_ saves the adjList of "person_knows_person" edges for the first edge chunk of the first vertex chunk, and the adjList type of the edges is "ordered_by_source". This adjList table consists of only two columns: one for the source and one for the destination; it can be saved in CSV, ORC, or Parquet files. + +.. note:: + + If the edges are ordered, there may also be offset chunks to construct the index for accessing edges of a single vertex. These chunks will store the start offset of each vertex's edges, see `./edge/person_knows_person/ordered_by_source/offset/chunk0`_ as an example. + + +How to Use GAR +------------------------ + +Construct information +````````````````````` +It is convenient to construct the GAR metadata and dump it to generate information files. We provide an `example program`_ located in the source code which shows how to construct and dump the files for graph information, vertex information and edge information. + +Also, the metadata of a graph can be constructed easily through reading the already existed information files, as the following code illustrates: + +.. code:: C++ + + // construct graph information from file + std::string path = ... // the path of the graph information file (e.g., ldbc_sample.graph.yml) + auto graph_info = GraphArchive::GraphInfo::Load(path).value(); + + // get vertex information + auto vertex_info = graph_info->GetVertexInfo("person"); + if (vertex_info != nullptr) { + // use vertex_info ... + } + + // get edge information + auto edge_info = graph_info->GetEdgeInfo("person", "knows", "person"); + if (edge_info != nullptr) { + // use edge_info ... + } + + +Read GAR files +`````````````` +GAR supports the flexible reading of graph data, e.g., allowing to read data of a single vertex, a vertex chunk, or all vertices with a specific label. In addition, necessary property groups can be selected to read and avoid reading all properties from the files. Furthermore, GAR provides convenient and flexible access to adjList, offset and property chunks for edges. + +As a simple case, the following example shows how to read all vertices with label "person" of the graph defined by "graph_info" and output the values of "id" and "firstName" for each vertex. + +.. code:: C++ + + graph_info = ... + auto vertices = GraphArchive::VerticesCollection::Make(graph_info, "person").value(); + + for (auto it = vertices->begin(); it != vertices->end(); ++it) { + // get a vertex and access its data + auto vertex = *it; + std::cout << "id=" << vertex.property("id").value() << ", firstName=" << vertex.property("firstName").value() << std::endl; + } + +The next example reads all edges with label "person_knows_person" from the above graph and outputs the end vertices for each edge. + +.. code:: C++ + + graph_info = ... + auto expect = GraphArchive::EdgesCollection::Make(graph_info, "person", "konws", "person", GraphArchive::AdjListType::ordered_by_source); + auto edges = expect.value(); + + for (auto it = edges->begin(); it != edges->end(); ++it) { + // get an edge and access its data + auto edge = *it; + std::cout << "src=" << edge.source() << ", dst=" << edge.destination() << std::endl; + } + +See also `C++ Reader API Reference `_. + +Write GAR files +``````````````` +As same with the readers, the GAR writers provide different-level methods to output the graph data in memory into GAR files. + +As the simplest cases, the fist example below adds vertices to **VerticesBuilder**, and then dumps the data to files; the second example constructs a collection of edges and then dumps them. + +.. code:: C++ + + vertex_info = ... + prefix = ... + GraphArchive::builder::VerticesBuilder builder(vertex_info, prefix); + + // add a vertex + GraphArchive::builder::Vertex v; + v.AddProperty("id", 933); + v.AddProperty("firstName", "Alice"); + builder.AddVertex(v); + // add other vertices + // ... + + // write to GAR files + builder.Dump(); + +.. code:: C++ + + edge_info = ... + prefix = ... + vertices_num = ... + GraphArchive::builder::EdgesBuilder builder(edge_info, prefix, GraphArchive::AdjListType::ordered_by_source, vertices_num); + + // add an edge (0 -> 3) + GraphArchive::builder::Edge e(0, 3); + e.AddProperty("creationDate", "2011-07-20T20:02:04.233+0000"); + builder.AddEdge(e); + // add other edges + // ... + + // write to GAR files + builder.Dump(); + +See also `C++ Writer API Reference `_. + +A PageRank Example +`````````````````` +Here we will go through an example of out-of-core graph analytic algorithms based on GAR which calculates the PageRank. Please look `here `_ if you want a detailed explanation of the PageRank algorithm. And the source code can be found at `pagerank_example.cc`_. + +This program first reads in the graph information file to obtain the metadata; then, it constructs the vertex and edge collections to enable access to the graph. After that, an implementation of the PageRank algorithm is provided, with data for the vertices stored in memory, and the edges streamed through disk I/O. Finally, the vertex information with type "person" is extended to include a new property named "pagerank" (a new vertex information file named *person-new-pagerank.vertex.yml* is saved) and the **VerticesBuilder** is used to write the results to new generated data chunks. + +Please refer to `more examples `_ to learn about the other available case studies utilizing GraphAr. + +.. _Building Steps: https://github.com/alibaba/GraphAr/blob/main/README.rst#building-libraries + +.. _person.vertex.yml: https://github.com/GraphScope/gar-test/blob/main/ldbc_sample/csv/person.vertex.yml + +.. _person_knows_person.edge.yml: https://github.com/GraphScope/gar-test/blob/main/ldbc_sample/csv/person_knows_person.edge.yml + +.. _./vertex/person/firstName_lastName_gender/chunk0: https://github.com/GraphScope/gar-test/blob/main/ldbc_sample/csv/vertex/person/firstName_lastName_gender/chunk0 + +.. _test data: https://github.com/GraphScope/gar-test/blob/main/ldbc_sample/ + +.. _./edge/person_knows_person/ordered_by_source/creationDate/part0/chunk0: https://github.com/GraphScope/gar-test/blob/main/ldbc_sample/csv/edge/person_knows_person/ordered_by_source/creationDate/part0/chunk0 + +.. _./edge/person_knows_person/ordered_by_source/adj_list/part0/chunk0: https://github.com/GraphScope/gar-test/blob/main/ldbc_sample/csv/edge/person_knows_person/ordered_by_source/adj_list/part0/chunk0 + +.. _./edge/person_knows_person/ordered_by_source/offset/chunk0: https://github.com/GraphScope/gar-test/blob/main/ldbc_sample/csv/edge/person_knows_person/ordered_by_source/offset/chunk0 + +.. _example program: https://github.com/alibaba/GraphAr/blob/main/cpp/examples/construct_info_example.cc + +.. _pagerank_example.cc: https://github.com/alibaba/GraphAr/blob/main/cpp/examples/pagerank_example.cc diff --git a/_sources/cpp/index.rst.txt b/_sources/cpp/index.rst.txt new file mode 100644 index 000000000..34b6b6b63 --- /dev/null +++ b/_sources/cpp/index.rst.txt @@ -0,0 +1,9 @@ +C++ Library +============================ + +.. toctree:: + :maxdepth: 2 + + Getting Started + Examples + Reference diff --git a/_sources/cpp/reference/index.rst.txt b/_sources/cpp/reference/index.rst.txt new file mode 100644 index 000000000..297e2317a --- /dev/null +++ b/_sources/cpp/reference/index.rst.txt @@ -0,0 +1,284 @@ +API Reference +================== + +.. _cpp-api: + +.. default-domain:: cpp + +Graph Info +----------- + +.. doxygenclass:: GraphArchive::Property + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::PropertyGroup + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::AdjacentList + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::VertexInfo + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::EdgeInfo + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::GraphInfo + :members: + :undoc-members: + + +Readers +--------------------- + +Chunk Info Reader +~~~~~~~~~~~~~~~~~ + +.. doxygenclass:: GraphArchive::VertexPropertyChunkInfoReader + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::AdjListChunkInfoReader + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::AdjListPropertyChunkInfoReader + :members: + :undoc-members: + +Arrow Chunk Reader +~~~~~~~~~~~~~~~~~~ + +.. doxygenclass:: GraphArchive::VertexPropertyArrowChunkReader + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::AdjListArrowChunkReader + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::AdjListOffsetArrowChunkReader + :members: + :undoc-members: + +Vertices Collection +~~~~~~~~~~~~~~~~~~~ + +.. doxygenclass:: GraphArchive::Vertex + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::VertexIter + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::VerticesCollection + :members: + :undoc-members: + +Edges Collection +~~~~~~~~~~~~~~~~~~ + +.. doxygenclass:: GraphArchive::Edge + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::EdgeIter + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::EdgesCollection + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::OBSEdgeCollection + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::OBDEdgesCollection + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::UBSEdgesCollection + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::UBDEdgesCollection + :members: + :undoc-members: + +Writer and Builder +--------------------- + +Chunk Writer +~~~~~~~~~~~~~~~~~ + +.. doxygenclass:: GraphArchive::VertexPropertyWriter + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::EdgeChunkWriter + :members: + :undoc-members: + +Builder +~~~~~~~~~~~~~~~~~~~ + +.. doxygenclass:: GraphArchive::builder::Vertex + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::builder::Edge + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::builder::VerticesBuilder + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::builder::EdgesBuilder + :members: + :undoc-members: + + +Types +-------- + +Id Type +~~~~~~~~~~~~~~~~~~~ + +.. doxygentypedef:: GraphArchive::IdType + +Data Type +~~~~~~~~~~~~~~~~~~~ + +.. doxygenclass:: GraphArchive::DataType + :members: + :undoc-members: + +File Type +~~~~~~~~~~~~~~~~~~~ +.. doxygenenum:: GraphArchive::FileType + +Adj List Type +~~~~~~~~~~~~~~~~~~~ +.. doxygenenum:: GraphArchive::AdjListType + +Validate Level +~~~~~~~~~~~~~~~~~~~ +.. doxygenenum:: GraphArchive::ValidateLevel + + +Utilities +--------- + +Result and Status +~~~~~~~~~~~~~~~~~~~ + +.. doxygentypedef:: GraphArchive::Result + +.. doxygenclass:: GraphArchive::Status + :members: + :undoc-members: + +FileSystem +~~~~~~~~~~~~~~~~~~~ + +.. doxygenclass:: GraphArchive::FileSystem + :members: + :undoc-members: + +.. doxygenfunction:: GraphArchive::FileSystemFromUriOrPath + +Yaml Parser +~~~~~~~~~~~~~~~~~~~ + +.. doxygenclass:: GraphArchive::Yaml + :members: + :undoc-members: + +Info Version +~~~~~~~~~~~~~~~~~~~ + +.. doxygenclass:: GraphArchive::InfoVersion + :members: + :undoc-members: + +Expression +~~~~~~~~~~~~~~~~~~~ + +.. doxygenclass:: GraphArchive::Expression + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::ExpressionProperty + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::ExpressionLiteral + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::ExpressionNot + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::ExpressionUnaryOp + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::ExpressionBinaryOp + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::ExpressionEqual + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::ExpressionNotEqual + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::ExpressionGreaterThan + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::ExpressionGreaterEqual + :members: + :undoc-members: + + +.. doxygenclass:: GraphArchive::ExpressionLessThan + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::ExpressionLessEqual + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::ExpressionAnd + :members: + :undoc-members: + +.. doxygenclass:: GraphArchive::ExpressionOr + :members: + :undoc-members: + +.. doxygenfunction:: GraphArchive::_Property(const Property&) +.. doxygenfunction:: GraphArchive::_Property(const std::string&) +.. doxygenfunction:: GraphArchive::_Literal +.. doxygenfunction:: GraphArchive::_Not +.. doxygenfunction:: GraphArchive::_Equal +.. doxygenfunction:: GraphArchive::_NotEqual +.. doxygenfunction:: GraphArchive::_GreaterThan +.. doxygenfunction:: GraphArchive::_GreaterEqual +.. doxygenfunction:: GraphArchive::_LessThan +.. doxygenfunction:: GraphArchive::_LessEqual +.. doxygenfunction:: GraphArchive::_And +.. doxygenfunction:: GraphArchive::_Or + diff --git a/_sources/developers/community.rst.txt b/_sources/developers/community.rst.txt new file mode 100644 index 000000000..6659c1014 --- /dev/null +++ b/_sources/developers/community.rst.txt @@ -0,0 +1,71 @@ +GraphAr Community +==================== + +We welcome participation from everyone and encourage you to join us, ask questions, and get involved. + +All participation in the GraphAr project is governed by the `GraphAr Code of Conduct`_. + +Questions? +----------- + +Mailing list +^^^^^^^^^^^^^ +The `GraphAr mailing list`_ is for discussion of GraphAr development and usage. + +Send an email to `graphar+subscribe@googlegroups.com `_ +to subscribe to the mailing list. + + +Tag Convention +""""""""""""""""" +The mailing list follow the convention of prefixing subjects with one or more tags in order to +clarify the scope and purpose of messages. For example: + +- [ANNOUNCE] GraphAr 0.1.0 released +- [DISCUSS][C++] How to implement a new API in C++? +- [DISCUSS][Format] How to support partition pruning? +- [Spark][Reader] Support the push-down of filters + +When emailing to the mail list, please prefix the subject line with one or more tags. +Depending the topic of your email, tags may include one or more: + +- Supported Environments: e.g., [C++], [Spark], [Java], etc. +- Specifications: e.g., [Format], [Reader], [Writer], [API], etc. + +You may also prefix your subject line with [DISCUSS] if your email is intended to prompt a discussion +rather than get an answer to a specific question. + +Slack +^^^^^^ +If you are not used to communicate with mailing list, you can also join the +`GraphAr Slack`_ to chat with other GraphAr users and developers. + +GitHub Issues +^^^^^^^^^^^^^ +We use GitHub issues as a way to ask questions and engage with the GraphAr developer community +and for maintaining a queue of development work and as the public record of work on the project. +We use the mailing list for development discussions, where a lengthy discussions is required. + +Community Meeting +^^^^^^^^^^^^^^^^^ +We host online meetings to provide spaces for synchronous discussions about the GraphAr project. +These discussions usually focus on topics of interest to developers who are contributing to GraphAr, +but we welcome users of GraphAr to join. + +The community meeting runs weekly, every Tuesday at 7:00 PM (UTC+8). The meeting is hosted on Ali Meeting. + +See the `community meeting notes`_ for the next meeting. + + +Contributing +------------ +As mentioned above, we use `GitHub `_ for our issue tracker and for source control. +See the `contribution guidelines `_ for more. + +.. _GraphAr Code of Conduct: https://github.com/alibaba/GraphAr/blob/main/CODE_OF_CONDUCT.md + +.. _GraphAr mailing list: https://groups.google.com/g/graphar + +.. _GraphAr Slack: https://join.slack.com/t/grapharworkspace/shared_invite/zt-1wh5vo828-yxs0MlXYBPBBNvjOGhL4kQ + +.. _community meeting notes: https://github.com/alibaba/GraphAr/wiki/Community-Meeting-Agenda diff --git a/_sources/developers/contributing.rst.txt b/_sources/developers/contributing.rst.txt new file mode 100644 index 000000000..3dc0f5e37 --- /dev/null +++ b/_sources/developers/contributing.rst.txt @@ -0,0 +1,4 @@ +.. This file is just a placeholder to refer the top-level CONTRIBUTING.rst + to sphinx doc workspace. + +.. include:: ../../CONTRIBUTING.rst diff --git a/_sources/developers/java-dev.rst.txt b/_sources/developers/java-dev.rst.txt new file mode 100644 index 000000000..62cbd26a5 --- /dev/null +++ b/_sources/developers/java-dev.rst.txt @@ -0,0 +1,108 @@ +Java Development +================ + +Introduction +------------ + +GraphAr Java library based on GraphAr C++ library and an efficient FFI +for Java and C++ called +`FastFFI `__. + +Source Code Level +~~~~~~~~~~~~~~~~~ + +- Interface + +- Class + +- JNI code + +- GraphAr C++ library + +If you want to use classes or functions of GraphAr C++ library through JAVA SDK, you only need to write interfaces with +annotations. After the interfaces are ready, the java code for the interfaces and the the C++ code which include JNI +code for native methods will be automatically generated by FastFFI.For +annotation's usage, please refer to +`FastFFI `__. + + + +Runtime Level +~~~~~~~~~~~~~ + +Interfaces and classes will be compiled to bytecode. Usually, JNI code will be compiled to bitcode as a part of +dynamic library which can be called by native methods directly. +If llvm4jni is enable, suitable method in JNI will be transferred to bytecode. + +For decoupling the implementation of C++ and Java, we use a bridge dynamic library called gar-jni to connect them, it +will integrate all C++ dependencies(e.g. JNI code, GraphAr C++ library and arrow C++) +and can be called by native methods in Java directly. +Most JNI code is generated by FastFFI, but some JNI code is written by ourselves, such as JNI code for +transferring VectorSchemaRoot into arrow::Table. + +To build the bridge dynamic library, here is main part of our CMakeLists.txt: + +.. code-block:: cmake + + # set auto-generated JNI code and handwriting JNI code as source files + file(GLOB SOURCES "${CMAKE_CURRENT_SOURCE_DIR}/target/generated-sources/annotations/*.cc" "${CMAKE_CURRENT_SOURCE_DIR}/target/generated-test-sources/test-annotations/*.cc" + "${CMAKE_CURRENT_SOURCE_DIR}/src/main/cpp/ffi/*.cc") + # remove auto-generated JNI code for specific method cause we have handwriting JNI code for it + list(REMOVE_ITEM SOURCES "${CMAKE_CURRENT_SOURCE_DIR}/target/generated-sources/annotations/jni_com_alibaba_graphar_arrow_ArrowTable_Static_cxx_0x58c7409.cc") + + set(LIBNAME "gar-jni") + + # find JNI related libraries + find_package(JNI REQUIRED) + include_directories(SYSTEM ${JAVA_INCLUDE_PATH}) + include_directories(SYSTEM ${JAVA_INCLUDE_PATH2}) + + # some JNI code depends on arrow + find_package(Arrow REQUIRED) + # build graphar-cpp in specific version + include(graphar-cpp) + build_graphar_cpp() + + # build the bridge JNI library + add_library(${LIBNAME} SHARED ${SOURCES}) + # include graphar-cpp headers + target_include_directories(${LIBNAME} SYSTEM BEFORE PRIVATE ${GAR_INCLUDE_DIR}) + # link graphar-cpp and arrow + target_link_libraries(${LIBNAME} ${CMAKE_JNI_LINKER_FLAGS} gar_shared) + target_link_libraries(${LIBNAME} ${CMAKE_JNI_LINKER_FLAGS} Arrow::arrow_static) + +More about usage of CMake, please refer to `CMake's official website `__. + +Building GraphAr Java +--------------------- + +Please refer to `GraphAr Java Library user guide <../user-guide/java-lib.html>`__. + +How To Test +----------- + +.. code-block:: bash + + $ export GAR_TEST_DATA=$PWD/../testing/ + $ mvn clean test + +This will build GraphAr C++ library internally for Java. If you already installed GraphAr C++ library in your system, +you can append this option to skip: ``-DbuildGarCPP=OFF``. + +Code Style +---------- + +We follow `AOSP Java code +style `__. To ensure +CI for checking code style will pass, please ensure check below is +success: + +.. code-block:: bash + + $ mvn spotless:check + +If there are violations, running command below to automatically format: + +.. code-block:: bash + + $ mvn spotless:apply diff --git a/_sources/file-format.rst.txt b/_sources/file-format.rst.txt new file mode 100644 index 000000000..f8d384df1 --- /dev/null +++ b/_sources/file-format.rst.txt @@ -0,0 +1,172 @@ +GraphAr File Format +============================ + +What is Property Graph +------------------------ + +GraphAr is designed for representing and storing the property graphs. Graph (in discrete mathematics) is a structure made of vertices and edges. Property graph is then a type of graph model where the vertices/edges could carry a name (also called as type or label) and some properties. Since carrying additional information than non-property graphs, the property graph is able to represent connections among data scattered across diverse data databases and with different schemas. Compared with the relational database schema, the property graph excels at showing data dependencies. Therefore, it is widely-used in modeling modern applications including social network analytics, data mining, network routing, scientific computing and so on. + +A property graph consists of vertices and edges, with each vertex contains a unique identifier and: + +- A text label that describes the vertex type. +- A collection of properties, with each property can be represented by a key-value pair. + +Each edge contains a unique identifier and: + +- The outgoing vertex (source). +- The incoming vertex (destination). +- A text label that describes the relationship between the two vertices. +- A collection of properties. + +The following is an example property graph containing two types of vertices ("person" and "comment") and three types of edges. + +.. image:: images/property_graph.png + :width: 700 + :align: center + :alt: property graph + + +Vertices in GraphAr +------------------------ + +Logical table of vertices +````````````````````````` +Each type of vertices (with the same label) constructs a logical vertex table, with each vertex assigned with a global index inside this type (called internal vertex id) starting from 0, corresponding to the row number of the vertex in the logical vertex table. An example layout for a logical table of vertices under the label "person" is provided for reference. + +Given an internal vertex id and the vertex label, a vertex is uniquely identifiable and its respective properties can be accessed from this table. The internal vertex id is further used to identify the source and destination vertices when maintaining the topology of the graph. + +.. image:: images/vertex_logical_table.png + :width: 650 + :align: center + :alt: vertex logical table + +.. note:: + + In the logical vertex table, some property can be marked as the primary key, such as the "id" column of the "person" table. + + +Physical table of vertices +`````````````````````````` +The logical vertex table will be partitioned into multiple continuous vertex chunks for enhancing the reading/writing efficiency. To maintain the ability of random access, the size of vertex chunks for the same label is fixed. To support to access required properties avoiding reading all properties from the files, and to add properties for vertices without modifying the existing files, the columns of the logical table will be divided into several column groups. + +Take the "person" vertex table as an example, if the chunk size is set to be 500, the logical table will be separated into sub-logical-tables of 500 rows with the exception of the last one, which may have less than 500 rows. The columns for maintaining properties will also be divided into distinct groups (e.g., 2 for our example). As a result, a total of 4 physical vertex tables are created for storing the example logical table, which can be seen from the following figure. + +.. image:: images/vertex_physical_table.png + :width: 650 + :align: center + :alt: vertex physical table + + +**Note**: For efficiently utilize the filter push-down of the payload file format like Parquet, the internal vertex id is stored in the payload file as a column. And since the internal vertex id is continuous, the payload file format can use the delta encoding for the internal vertex id column, which would not bring too much overhead for the storage. + +Edges in GraphAr +------------------------ + +Logical table of edges +`````````````````````` +For maintaining a type of edges (that with the same triplet of the source label, edge label, and destination label), a logical edge table is established. And in order to support quickly creating a graph from the graph storage file, the logical edge table could maintain the topology information in a way similar to CSR/CSC (learn more about `CSR/CSC `_), that is, the edges are ordered by the internal vertex id of either source or destination. In this way, an offset table is required to store the start offset for each vertex's edges, and the edges with the same source/destination will be stored continuously in the logical table. + +Take the logical table for "person likes person" edges as an example, the logical edge table looks like: + +.. image:: images/edge_logical_table.png + :width: 650 + :align: center + :alt: edge logical table + +Physical table of edges +``````````````````````` +As same with the vertex table, the logical edge table is also partitioned into some sub-logical-tables, with each sub-logical-table contains edges that the source (or destination) vertices are in the same vertex chunk. According to the partition strategy and the order of the edges, edges can be stored in GraphAr following one of the four types: + +- **ordered_by_source**: all the edges in the logical table are ordered and further partitioned by the internal vertex id of the source, which can be seen as the CSR format. +- **ordered_by_dest**: all the edges in the logical table are ordered and further partitioned by the internal vertex id of the destination, which can be seen as the CSC format. +- **unordered_by_source**: the internal id of the source vertex is used as the partition key to divide the edges into different sub-logical-tables, and the edges in each sub-logical-table are unordered, which can be seen as the COO format. +- **unordered_by_dest**: the internal id of the destination vertex is used as the partition key to divide the edges into different sub-logical-tables, and the edges in each sub-logical-table are unordered, which can also be seen as the COO format. + +After that, a sub-logical-table is further divided into edge chunks of a predefined, fixed number of rows (referred to as edge chunk size). Finally, an edge chunk is separated into physical tables in the following way: + +- an adjList table (which contains only two columns: the internal vertex id of the source and the destination). +- 0 or more edge property tables, with each table contains a group of properties. + +Additionally, there would be an offset table for **ordered_by_source** or **ordered_by_dest** edges. The offset table is used to record the starting point of the edges for each vertex. The partition of the offset table should be in alignment with the partition of the corresponding vertex table. The first row of each offset chunk is always 0, indicating the starting point for the corresponding sub-logical-table for edges. + +Take the "person knows person" edges to illustrate. Suppose the vertex chunk size is set to 500 and the edge chunk size is 1024, and the edges are **ordered_by_source**, then the edges could be saved in the following physical tables: + +.. image:: images/edge_physical_table1.png + :width: 650 + :align: center + :alt: edge physical table1 + +.. image:: images/edge_physical_table2.png + :width: 650 + :align: center + :alt: edge physical table2 + +.. tip:: + + When the edge type is **ordered_by_source**, the sorted adjList table together with the offset table can be used as CSR, supporting the fast access of the outgoing edges for a given vertex. Similarly, a CSC view can be constructed by sorting the edges by destination and recording corresponding offsets, supporting the fast access of the incoming edges for a given vertex. + + +File Format +------------------------ + +Information files +````````````````` +GraphAr uses two kinds of files to store a graph: a group of Yaml files to describe meta information; and data files to store actual data for vertices and edges. +A graph information file which named ".graph.yml" describes the meta information for a graph whose name is . The content of this file includes: + +- the graph name; +- the root directory path of the data files; +- the vertex information and edge information files included; +- the version of GraphAr. + +A vertex information file which named "

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