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Kiến trúc của HDFS – PhamBinh.net
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- Summary of article content: Articles about Kiến trúc của HDFS – PhamBinh.net Không giống như namenode, datanode không yêu cầu tính sẵn sàng cao, bởi trong một hadoop cluster thì thường có nhiều datanode. Vì vậy mà các máy datanode … …
- Most searched keywords: Whether you are looking for Kiến trúc của HDFS – PhamBinh.net Không giống như namenode, datanode không yêu cầu tính sẵn sàng cao, bởi trong một hadoop cluster thì thường có nhiều datanode. Vì vậy mà các máy datanode …
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I Cơ chế Master – Slave
II Vai trò của Namenode Datanode và Secondary namenode
III Block là gì
IV Cơ chế quản lý các bản sao
V Tổng kết
Hadoop Data Node Activity Test
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- Summary of article content: Articles about Hadoop Data Node Activity Test NameNode is the master node in the Apache Hadoop HDFS Architecture that maintains and manages the blocks present on the DataNodes (slave nodes). NameNode is a … …
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Hadoop: DataNode không bắt đầu
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- Summary of article content: Articles about Hadoop: DataNode không bắt đầu Khi tiếp tục chơi với Mahout, cuối cùng tôi đã quyết định từ bỏ sử dụng hệ thống tệp cục bộ của mình và sử dụng Hadoop cục bộ thay vì điều … …
- Most searched keywords: Whether you are looking for Hadoop: DataNode không bắt đầu Khi tiếp tục chơi với Mahout, cuối cùng tôi đã quyết định từ bỏ sử dụng hệ thống tệp cục bộ của mình và sử dụng Hadoop cục bộ thay vì điều … Khi tiếp tục chơi với Mahout, cuối cùng tôi đã quyết định từ bỏ sử dụng hệ thống tệp cục bộ của mình và sử dụng Hadoop cục bộ thay vì điều đó dường như ít ma sát hơn khi làm theo bất kỳ ví…big data,hadoop,datanode,quản tượng,dữ liệu lớn
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Docker Hub
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- Summary of article content: Articles about Docker Hub Hadoop datanode of a hadoop cluster. … docker stack deploy -c docker-compose-v3.yml hadoop. docker-compose creates a docker network that can be found by … …
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configuration – Datanode process not running in Hadoop – Stack Overflow
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- Summary of article content: Articles about configuration – Datanode process not running in Hadoop – Stack Overflow I set up and configured a multi-node Hadoop cluster using this tutorial. … As you can see, there’s no datanode process running. I tried configuring a single- … …
- Most searched keywords: Whether you are looking for configuration – Datanode process not running in Hadoop – Stack Overflow I set up and configured a multi-node Hadoop cluster using this tutorial. … As you can see, there’s no datanode process running. I tried configuring a single- …
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Thiết lập Multi Node Clustor trong Hadoop 2.X
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Thiết lập Multi Node Clustor trong Hadoop 2.X
Một Multi Node Cluster trong Hadoop chứa hai hoặc nhiều DataNode … BƯỚC 3: Mở file hosts để thêm Master node và Data node với địa chỉ IP … … - Most searched keywords: Whether you are looking for
Thiết lập Multi Node Clustor trong Hadoop 2.X
Một Multi Node Cluster trong Hadoop chứa hai hoặc nhiều DataNode … BƯỚC 3: Mở file hosts để thêm Master node và Data node với địa chỉ IP … Multi Node ClustorHướng dẫn chi tiết với 23 bước thiết lập Multi Node Clustor trong Hadoop 2.X. Series Master BigData - Table of Contents:
Multi Node Cluster trong Hadoop 2x
Hướng dẫn 23 Bước Thiết lập Multi Node Cluster trong Hadoop
Hadoop HDFS service stops frequently due to Datanode(s) Crashing
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- Summary of article content: Articles about Hadoop HDFS service stops frequently due to Datanode(s) Crashing A Hadoop Datanode is a core component of Hadoop and will require more Java heap space the more data is being processed. …
- Most searched keywords: Whether you are looking for Hadoop HDFS service stops frequently due to Datanode(s) Crashing A Hadoop Datanode is a core component of Hadoop and will require more Java heap space the more data is being processed. During normal operation using IBM BigInsights, the Hadoop HDFS may become unavailable with the following error being reported in the Datanode log “java.lang.OutOfMemoryError: Java heap space”.biginsights datanode crashing Java heap space
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Problem
Symptom
Cause
Environment
Diagnosing The Problem
Resolving The Problem
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HDFS Architecture | Facebook
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- Summary of article content: Articles about HDFS Architecture | Facebook Nếu 1 DataNode chết, NameNode sẽ thực hiện replicate các block của … do đó mà ở phiên bản Hadoop 2x đã giới thiệu kiến trúc NameNode High Availability. …
- Most searched keywords: Whether you are looking for HDFS Architecture | Facebook Nếu 1 DataNode chết, NameNode sẽ thực hiện replicate các block của … do đó mà ở phiên bản Hadoop 2x đã giới thiệu kiến trúc NameNode High Availability. 1. Kiến trúc HDFS
HDFS được thiết kế dựa trên mô hình master/slave, trong đó master là NameNode và slave là DataNode.
NameNode là
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Hadoop Data Node Activity Test
Hadoop Data Node Activity Test
Apache Hadoop HDFS Architecture follows a Master/Slave Architecture, where a cluster comprises of a single NameNode (Master node) and all the other nodes are DataNodes (Slave nodes).
DataNodes are the slave nodes in HDFS. The actual data is stored on DataNodes. A functional filesystem has more than one DataNode, with data replicated across them.
On startup, a DataNode connects to the NameNode; spinning until that service comes up. It then responds to requests from the NameNode for filesystem operations. Local and remote client applications can talk directly to a DataNode, once the NameNode has provided the location of the data. Similarly, MapReduce operations farmed out to TaskTracker instances near a DataNode, talk directly to the DataNode to access the files. Also, the DataNodes periodically perform block verification to identify corrupt blocks.
The NameNode also initiates replication of blocks on the DataNodes as and when necessary. Moreover, DataNodes also cache blocks in off-heap caches based on caching instructions they receive from the NameNode.
Each of these operations impose load on a DataNode. Since I/O load is uniformly distributed across the DataNodes in a Hadoop cluster, an administrator needs to closely observe the I/O activity on every DataNode, so they can promptly capture load-balancing irregularities. Administrators should also assess how each DataNode is processing the I/O requests, so that they can proactively detect bottlenecks in request servicing in any DataNode. They should also check if block verification has occurred on any DataNode, so they can quickly detect verification failures. Furthermore, as block caching is a healthy exercise, administrators should also ensure that adequate blocks are cached on every DataNode. With the help of Hadoop Data Node Activity test, administrators can monitor all the activities discussed above on every DataNode. In the process, administrators can rapidly identify overloaded DataNodes, slow DataNodes, those where block verification has failed, and those where caching is sub-optimal.
Target of the test : A Hadoop cluster
Agent deploying the test : A remote agent
Outputs of the test : One set of the results for each DataNode in the target Hadoop cluster
Configurable parameters for the test Parameter Description Test Period How often should the test be executed. Host The IP address of the NameNode that processes client connections to the cluster. NameNode is the master node in the Apache Hadoop HDFS Architecture that maintains and manages the blocks present on the DataNodes (slave nodes). NameNode is a very highly available server that manages the File System Namespace and controls access to files by clients. Port The port at which the NameNode accepts client connections. NameNode is the master node in the Apache Hadoop HDFS Architecture that maintains and manages the blocks present on the DataNodes (slave nodes). NameNode is a very highly available server that manages the File System Namespace and controls access to files by clients. By default, the NameNode’s client connection port is 8020. Name Node Web Port The eG agent collects metrics using Hadoop’s WebHDFS REST API. While some of these API calls pull metrics from the NameNode, some others get metrics from the resource manager. NameNode is the master node in the Apache Hadoop HDFS Architecture that maintains and manages the blocks present on the DataNodes (slave nodes). NameNode is a very highly available server that manages the File System Namespace and controls access to files by clients. To run API commands on the NameNode and pull metrics, the eG agent needs access to the NameNode’s web port. To determine the correct web port of the NameNode, do the following: Open the hdfs-default.xml file in the hadoop/conf/app directory.
file in the hadoop/conf/app directory. Look for the dfs.namenode.http-address parameter in the file.
parameter in the file. This parameter is configured with the IP address and base port where the DFS NameNode web user interface listens on. The format of this configuration is:
: . Given below is a sample configuration: 192.168.10.100:50070 Configure the in the specification as the Name Node Web Port. In the case of the above sample configuration, this will be 50070. Name Node User Name The eG agent collects metrics using Hadoop’s WebHDFS REST API. While some of these API calls pull metrics from the NameNode, some others get metrics from the resource manager. NameNode is the master node in the Apache Hadoop HDFS Architecture that maintains and manages the blocks present on the DataNodes (slave nodes). NameNode is a very highly available server that manages the File System Namespace and controls access to files by clients. In some Hadoop configurations, a simple authentication user name may be required for running API commands and collecting metrics from the NameNode. When monitoring such Hadoop installations, specify the name of the simple authentication user here. If no such user is available/required, then do not disturb the default value none of this parameter. Resource Manager IP and Resource Manager Web Port The eG agent collects metrics using Hadoop’s WebHDFS REST API. While some of these API calls pull metrics from the NameNode, some others get metrics from the resource manager. The YARN Resource Manager Service (RM) is the central controlling authority for resource management and makes resource allocation decisions. To pull metrics from the resource manager, the eG agents first needs to connect to the resource manager. For this, you need to configure this test with the IP address/host name of the resource manager and its web port. Use the Resource Manager IP and Resource Manager Web Port parameters to configure these details. To determine the IP/host name and web port of the resource manager, do the following: Open the yarn-site.xml file in the /opt/mapr/hadoop/hadoop-2. x.x/etc/hadoop directory. file in the /opt/mapr/hadoop/hadoop-2. x.x/etc/hadoop directory. Look for the yarn.resourcemanager.webapp.address parameter in the file.
parameter in the file. This parameter is configured with the IP address/host name and web port of the resource manager. The format of this configuration is:
: . Given below is a sample configuration: 192.168.10.100:8080 Configure the in the specification as the Resource Manager IP, and the as the Resource Manager Web Port. In the case of the above sample configuration, this will be 8080. Resource Manager Username The eG agent collects metrics using Hadoop’s WebHDFS REST API. While some of these API calls pull metrics from the NameNode, some others get metrics from the resource manager. The YARN Resource Manager Service (RM) is the central controlling authority for resource management and makes resource allocation decisions. In some Hadoop configurations, a simple authentication user name may be required for running API commands and collecting metrics from the resource manager. When monitoring such Hadoop installations, specify the name of the simple authentication user here. If no such user is available/required, then do not disturb the default value none of this parameter.
Hadoop: DataNode không bắt đầu
Khi tiếp tục chơi với Mahout, cuối cùng tôi đã quyết định từ bỏ sử dụng hệ thống tệp cục bộ của mình và sử dụng Hadoop cục bộ thay vì điều đó dường như ít ma sát hơn khi làm theo bất kỳ ví dụ nào.
Thật không may, tất cả các nỗ lực của tôi để tải lên bất kỳ tệp nào từ hệ thống tệp cục bộ của tôi lên HDFS đã được đáp ứng với ngoại lệ sau:
java.io.IOException: File /user/markneedham/book2.txt could only be replicated to 0 nodes, instead of 1 at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.getAdditionalBlock(FSNamesystem.java:1448) at org.apache.hadoop.hdfs.server.namenode.NameNode.addBlock(NameNode.java:690) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) at java.lang.reflect.Method.invoke(Method.java:597) at org.apache.hadoop.ipc.WritableRpcEngine$Server.call(WritableRpcEngine.java:342) at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:1350) at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:1346) at java.security.AccessController.doPrivileged(Native Method) at javax.security.auth.Subject.doAs(Subject.java:396) at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:742) at org.apache.hadoop.ipc.Server$Handler.run(Server.java:1344) at org.apache.hadoop.ipc.Client.call(Client.java:905) at org.apache.hadoop.ipc.WritableRpcEngine$Invoker.invoke(WritableRpcEngine.java:198) at $Proxy0.addBlock(Unknown Source) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) at java.lang.reflect.Method.invoke(Method.java:597) at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:82) at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:59) at $Proxy0.addBlock(Unknown Source) at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.locateFollowingBlock(DFSOutputStream.java:928) at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.nextBlockOutputStream(DFSOutputStream.java:811) at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.run(DFSOutputStream.java:427)
Tôi cuối cùng đã nhận ra, từ tìm kiếm tại đầu ra của JPS , rằng DataNode đã không thực sự bắt đầu lên mà giải thích được thông báo lỗi tôi đã nhìn thấy.
Nhìn nhanh vào các tệp nhật ký cho thấy những gì đang xảy ra:
/usr/local/Cellar/hadoop/2.7.1/libexec/logs/hadoop-markneedham-datanode-mark-mbp-4.zte.com.cn.log
2016-07-21 18:58:00,496 WARN org.apache.hadoop.hdfs.server.common.Storage: java.io.IOException: Incompatible clusterIDs in /usr/local/Cellar/hadoop/hdfs/tmp/dfs/data: namenode clusterID = CID-c2e0b896-34a6-4dde-b6cd-99f36d613e6a; datanode clusterID = CID-403dde8b-bdc8-41d9-8a30-fe2dc951575c 2016-07-21 18:58:00,496 FATAL org.apache.hadoop.hdfs.server.datanode.DataNode: Initialization failed for Block pool
(Datanode Uuid unassigned) service to /0.0.0.0:8020. Exiting. java.io.IOException: All specified directories are failed to load. at org.apache.hadoop.hdfs.server.datanode.DataStorage.recoverTransitionRead(DataStorage.java:477) at org.apache.hadoop.hdfs.server.datanode.DataNode.initStorage(DataNode.java:1361) at org.apache.hadoop.hdfs.server.datanode.DataNode.initBlockPool(DataNode.java:1326) at org.apache.hadoop.hdfs.server.datanode.BPOfferService.verifyAndSetNamespaceInfo(BPOfferService.java:316) at org.apache.hadoop.hdfs.server.datanode.BPServiceActor.connectToNNAndHandshake(BPServiceActor.java:223) at org.apache.hadoop.hdfs.server.datanode.BPServiceActor.run(BPServiceActor.java:801) at java.lang.Thread.run(Thread.java:745) 2016-07-21 18:58:00,497 WARN org.apache.hadoop.hdfs.server.datanode.DataNode: Ending block pool service for: Block pool (Datanode Uuid unassigned) service to /0.0.0.0:8020 2016-07-21 18:58:00,602 INFO org.apache.hadoop.hdfs.server.datanode.DataNode: Removed Block pool (Datanode Uuid unassigned) 2016-07-21 18:58:02,607 WARN org.apache.hadoop.hdfs.server.datanode.DataNode: Exiting Datanode 2016-07-21 18:58:02,608 INFO org.apache.hadoop.util.ExitUtil: Exiting with status 0 2016-07-21 18:58:02,610 INFO org.apache.hadoop.hdfs.server.datanode.DataNode: SHUTDOWN_MSG: Tôi không chắc làm thế nào các clusterID của tôi không đồng bộ, mặc dù tôi mong đợi vì tôi đã định dạng lại HDFS mà không nhận ra ở một số giai đoạn. Có nhiều cách khác để giải quyết vấn đề này nhưng cách nhanh nhất đối với tôi là chỉ cần nuke thư mục dữ liệu của DataNode mà tệp nhật ký đã nói với tôi ở đây:
sudo rm -r /usr/local/Cellar/hadoop/hdfs/tmp/dfs/data/current
Sau đó, tôi đã chạy lại tập lệnh hstart mà tôi đã đánh cắp từ hướng dẫn này và mọi thứ, kể cả DataNode lần này, đã khởi động chính xác:
$ jps 26736 NodeManager 26392 DataNode 26297 NameNode 26635 ResourceManager 26510 SecondaryNameNode
Và bây giờ tôi có thể tải lại các tệp cục bộ lên HDFS. #thắng lợi!
Datanode process not running in Hadoop
I set up and configured a multi-node Hadoop cluster using this tutorial.
When I type in the start-all.sh command, it shows all the processes initializing properly as follows:
starting namenode, logging to /usr/local/hadoop/libexec/../logs/hadoop-root-namenode-jawwadtest1.out jawwadtest1: starting datanode, logging to /usr/local/hadoop/libexec/../logs/hadoop-root-datanode-jawwadtest1.out jawwadtest2: starting datanode, logging to /usr/local/hadoop/libexec/../logs/hadoop-root-datanode-jawwadtest2.out jawwadtest1: starting secondarynamenode, logging to /usr/local/hadoop/libexec/../logs/hadoop-root-secondarynamenode-jawwadtest1.out starting jobtracker, logging to /usr/local/hadoop/libexec/../logs/hadoop-root-jobtracker-jawwadtest1.out jawwadtest1: starting tasktracker, logging to /usr/local/hadoop/libexec/../logs/hadoop-root-tasktracker-jawwadtest1.out jawwadtest2: starting tasktracker, logging to /usr/local/hadoop/libexec/../logs/hadoop-root-tasktracker-jawwadtest2.out
However, when I type the jps command, I get the following output:
31057 NameNode 4001 RunJar 6182 RunJar 31328 SecondaryNameNode 31411 JobTracker 32119 Jps 31560 TaskTracker
As you can see, there’s no datanode process running. I tried configuring a single-node cluster but got the same problem. Would anyone have any idea what could be going wrong here? Are there any configuration files that are not mentioned in the tutorial or I may have looked over? I am new to Hadoop and am kinda lost and any help would be greatly appreciated.
EDIT: hadoop-root-datanode-jawwadtest1.log:
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