fix: Fix typos (#4523)

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## What do these changes do?
Fix typos
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## Related issue number

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Fixes

Author: co63oc

analytical_engine/core/context/context_protocols.h

@@ -36,7 +36,7 @@ enum class ContextDataType {
 };
 
 /* N.B. These values should be the same as vineyard::TypeToInt::value. Because
- * theses values are used to decode in Python side. Refer:
+ * these values are used to decode in Python side. Refer:
  * python.graphscope.framework.utils._to_numpy_dtype
  */
 inline int ContextDataTypeToInt(ContextDataType type) {

analytical_engine/java/grape-giraph/src/main/java/com/alibaba/graphscope/parallel/netty/request/serialization/WritableRequestEncoder.java

@@ -106,7 +106,7 @@ public void write(ChannelHandlerContext ctx, Object msg, ChannelPromise promise)
                 buf.setInt(0, buf.writerIndex() - SIZE_OF_INT);
                 if (logger.isDebugEnabled()) {
                     logger.debug(
-                            "Encode msg, type: [{}], writen bytes: [{}]",
+                            "Encode msg, type: [{}], written bytes: [{}]",
                             request.getRequestType().getClazz().getName(),
                             buf.readableBytes());
                 }
@@ -161,7 +161,7 @@ public void write(ChannelHandlerContext ctx, Object msg, ChannelPromise promise)
 //
 //            out.setInt(0, out.writerIndex() - SIZE_OF_INT);
 //            logger.info("Encode msg, type: " + request.getRequestType().getClazz().getName() + ",
-// writen bytes: " + out.readableBytes());
+// written bytes: " + out.readableBytes());
 //        }
 //        else {
 //            logger.error("Encoder: got instance " + msg + ", expect a WritableRequest");

analytical_engine/java/grape-graphx/src/main/java/com/alibaba/graphscope/utils/MPIUtils.java

@@ -264,7 +264,7 @@ public static <VD, ED> String[] loadFragment(
             String ipcSocket,
             Class<? extends VD> vdClass,
             Class<? extends ED> edClass) {
-        logger.info("Try to load fragment from raw datas: {}", Arrays.toString(rawDataIds));
+        logger.info("Try to load fragment from raw data: {}", Arrays.toString(rawDataIds));
         int numWorkers = rawDataIds.length;
         logger.info("running mpi with {} workers", numWorkers);
         String hostNameAndSlots = generateHostNameAndSlotsFromIDs(rawDataIds);

analytical_engine/java/grape-graphx/src/main/java/com/alibaba/graphscope/utils/VertexDataUtils.java

@@ -72,7 +72,7 @@ private static <VD> Tuple2<FFIByteVector, FFIIntVector> fillStringVertexArray(
         ffiByteVectorOutput.finishSetting();
         long writenBytes = ffiByteVectorOutput.bytesWritten();
         logger.info(
-                "write data array {} of type {}, writen bytes {}",
+                "write data array {} of type {}, written bytes {}",
                 size,
                 clz.getName(),
                 writenBytes);

analytical_engine/java/grape-jdk/src/main/java/com/alibaba/graphscope/fragment/EdgecutFragment.java

@@ -27,7 +27,7 @@
  * and vertex-cut, please refers to
  * https://spark.apache.org/docs/1.6.2/graphx-programming-guide.html#optimized-representation
  *
- * <p>If we have an edge a-&lt;b cutted by the partitioner, and a is in frag_0, and b in frag_1.
+ * <p>If we have an edge a-&lt;b cut by the partitioner, and a is in frag_0, and b in frag_1.
  * Then:a-&lt;b is a crossing edge, a is an inner_vertex in frag_0, b is an outer_vertex in frag_0.
  *
  * @param <OID_T> original vertex id type.

analytical_engine/java/grape-jdk/src/main/java/com/alibaba/graphscope/parallel/ParallelMessageManager.java

@@ -469,7 +469,7 @@ void sendMsgThroughIEdgesArrowProjected(
 
     /**
      * Parallel processing the messages received from last super step. The user just need to provide
-     * a lamba consumer.
+     * a lambda consumer.
      *
      * @param <MSG_T>     message type.
      * @param frag        fragment.

analytical_engine/java/performance.md

@@ -8,7 +8,7 @@ In most cases, the performance gap is below *2x*, moreover, when running pageran
 
 ## Evaluation Settings
 
-The evaluation is conducted on a cluster of 4 instances, each of them is equiped with following hardwares.
+The evaluation is conducted on a cluster of 4 instances, each of them is equipped with following hardwares.
 
 - CPU: Intel(R) Xeon(R) Platinum 8163 CPU @ 2.50GHz, 96 cores
 - Memory: 400GB

coordinator/gscoordinator/flex/controllers/data_source_controller.py

@@ -47,7 +47,7 @@ def get_datasource_by_id(graph_id):  # noqa: E501
 def unbind_edge_datasource(graph_id, type_name, source_vertex_type, destination_vertex_type):  # noqa: E501
     """unbind_edge_datasource
 
-    Unbind datas ource on an edge type # noqa: E501
+    Unbind datas source on an edge type # noqa: E501
 
     :param graph_id: 
     :type graph_id: str

coordinator/gscoordinator/flex/openapi/openapi.yaml

@@ -1126,7 +1126,7 @@ paths:
       x-openapi-router-controller: gscoordinator.flex.controllers.data_source_controller
   /api/v1/graph/{graph_id}/datasource/edge/{type_name}:
     delete:
-      description: Unbind datas ource on an edge type
+      description: Unbind datas source on an edge type
       operationId: unbind_edge_datasource
       parameters:
       - explode: false

flex/bin/bulk_loader.cc

@@ -128,7 +128,7 @@ int main(int argc, char** argv) {
     return -1;
   }
 
-  // check whether parallelism, build_csr_in_mem, use_mmap_vector are overriden
+  // check whether parallelism, build_csr_in_mem, use_mmap_vector are overridden
   if (vm.count("parallelism")) {
     loading_config_res.value().SetParallelism(vm["parallelism"].as<uint32_t>());
   }