Hi @Zack Riesland , thanks for the clarification. I understand now that s3-dist-cp is used both coming and going, and I agree it doesn't seem to have any renaming capability built in. I strongly suspect there's a way to control the filenames by controlling the Hive partition name, but I'm not a Hive expert and maybe the application benefits from using the same partition name for raw and clean data. Here's what I found that might help: a StackOverflow query that confirms your complaint that CLI rename of HDFS files is slow, and confirms my sense that it shouldn't be: https://stackoverflow.com/questions/14736017/batch-rename-in-hadoopThe second responder (Robert) wrote a 1-page java program that uses the hadoop java api, and shows that it can rename several thousand files in the same 4 seconds that the CLI takes to rename one file. This suggests that the majority of time is taken up by the connection protocol, and the java program can use a single connection for multiple operations. I also looked at options for using Python similarly. It appears that the hdfs3 library would do quite nicely, although you have to separately install the underlying C library opensourced by Pivotal. Full docs for installation and api are given in the linked doc. Please note it probably would not work to use the similarly-named "hdfs" library, because the latter uses the WebHDFS interface, which is unlikely to be faster than the CLI (although I didn't test it). Since I'm a Hortonworks employee, I should point out that the above opensource references are not supported or endorsed by the company, and are used at your own risk. I did look over the Java program and it looks fine to me, but that's my personal opinion, not the company's. Good luck. If that doesn't get you where you need to be, and since your profile popup indicates you are a Hortonworks support customer, you'd be welcome to open a support request for advice from an SE. ... View more

@zackriesland , you don't say what application you're using to pull and clean the new data on HDFS. The name pattern "data_day=01-01-2017/000000_0" is under control of that application, and can be configured there. It's not an HDFS thing; HDFS is just a file system, and stores files under whatever filenames the app tells it to use. If it is not acceptable to change the naming configuration in that app, there are other possible approaches. You say that renaming the files takes 3 seconds per chunk file. Am I correct in assuming that is using S3 commands to rename the file after uploading to S3? If so, you could try renaming the files on HDFS before uploading to S3. You can use the "hadoop fs -mv" CLI command, very much like the linux "mv" command, to rename files on HDFS from a script, and it should not take any 3 seconds. You can use this command in a shellscript while logged into any server in the HDFS cluster. Another way to run such a script is on a workstation with, for instance, the Python HDFS Client library installed, and configured to be able to talk to the HDFS cluster. Finally, you're probably aware that filenames in S3 are actually just strings, and the slash ("/") to delimit "folders" is purely convention, except for the delimiter between the bucket name and the rest of the filename. There aren't actually any folders below the level of the bucket. So changing the "dest" location for the s3-dist-cp command to put it in a sub-folder is really just the same as specifying a prefix (the name of the "sub-folder") on the filename. Use the date, or a serial number, or whatever convention you want so each invocation of s3-dist-cp puts its files on S3 with a different prefix. It seems that would also fulfill the requirement above. Hope one or another of these ideas helps. ... View more

There's a third option: You could use the InvokeHTTP processor. In it, use the Expression Language to construct the RemoteURL, to directly build an AWS S3 web API call, to get the listing you need. AWS web API is documented here: http://docs.aws.amazon.com/AmazonS3/latest/API/Welcome.html However, as that document says, "Making REST API calls directly from your code can be cumbersome. It requires you to write the necessary code to calculate a valid signature to authenticate your requests. We recommend the following alternatives instead: \[AWS SDK or AWS CLI\]" -- which are the approaches I gave above. ... View more

Hi @Identity One , You're right, it's unfortunate the ListS3 processor doesn't accept an incoming flowfile to provide a prefix. You might want to enter an Apache jira (https://issues.apache.org/jira/projects/NIFI : "Create" button) requesting such a feature. In the meantime, you can use the ExecuteScript processor to construct and run an appropriate s3 list command, with prefix, in response to incoming flowfiles. You can use any of the ExecuteScript allowed scripting languages that have a corresponding AWS SDK client library; see https://aws.amazon.com/tools/#sdk -- looks like the choices are python, ruby, or js. If you click through any of those links in the aws sdk page, under "Documentation" : "Code Examples" : "Amazon S3 Examples", it should help. For example, the Python S3 examples are here: https://boto3.readthedocs.io/en/latest/guide/s3-examples.html . The "list" api is here: https://boto3.readthedocs.io/en/latest/reference/services/s3.html#S3.Client.list_objects_v2 If you're not into scripting to the SDK, you could use your favorite scripting language's escape to shell (like Python's 'subprocess' feature), and invoke the AWS S3 command-line-interface commands (which you're evidently already familiar with). It's crude (and slower) but it would work 🙂 Hope this helps. ... View more

Hi @Sedat Kestepe, regarding "third party and custom apps", this is of course outside the scope of my expertise. But I think that, yes, you will have to find out (a) whether they are even capable of being configured to use non-default network; (b) if so, how to so configure them; and (c) if desired, whether they can use the "0.0.0.0" config, meaning "listen on all networks" -- which is often a cheap way to get desirable results, altho in some environments it has security implications. Your second question is "How is service bound to preferred interface." Let's be clear that there are two viewpoints, the service and the client. From the service's viewpoint, "binding interface" really means, "what ip address(es) and port number shall I LISTEN on for connection requests?" Regrettably there is no uniformity in how each application answers that question, even among the Hadoop Stack components -- else that article could have been about half the length! Some applications aren't configurable and always use the host's default ip address, and a fixed port number. Sometimes the port number is just the starting point, where the app and client negotiate yet another port number for each connection. Sometimes a list of ip addresses can be configured, to listen on a specific subset of networks available on multi-homed servers. Sometimes the "magic" address "0.0.0.0" is understood to mean "listen on ALL available network ip addresses". (This concept is integrated with most tcp/ip libraries that applications might use.) Almost always, though, a single application port number is used by a given service, which means only one instance of the service per host (although it can be multi-threaded if it uses that port negotiation trick). How the ip address(es) and port number are specified, in one or several config parameters, is up to the application. As noted in the article, sometimes Hadoop Stack services even use their own hostname and run it through DNS to obtain an ip address; the result is dependent on which DNS service is queried, which in turn may be configured by yet another parameter (see for example the Kerberos _HOST resolution feature). From the client's viewpoint, it must answer the question "where do I FIND that service?" Clients are usually much simpler and are configured with a single ip address and port number where it should attempt to contact a service. That isn't necessarily so; clients often have lists of servers that they try to connect to in sequence, so they could have multiple ip addresses for the same server. But usually we prefer to put complexity in services and keep clients as simple as possible, because clients usually run in less controlled environments than services. It never makes sense to hand "0.0.0.0" to a client, because the client is initiating, not listening. The client has to know SPECIFICALLY where to find the service, and it is typically (tho not necessarily) different than the client's host server. We encourage use of a hostname rather than ip address for this purpose, at which point the client will obtain the ip address from its default DNS server, or (in some cases) a configuration-specified DNS server. Regarding your third question, in the "Theory of Operations" section, items 5, 6, and 7 should be taken together. #5 is an example of why it is useful and even necessary to have the same hostname on all networks: "external clients [can] find and talk to Datanodes on an external (client/server) network, while Datanodes find and talk to each other on an internal (intra-cluster) network." If the particular Datanode has a different hostname on the two networks, all parties become confused, because they may tell each other to talk to a different Datanode by name. #6 actually gives you an escape hatch: "(Network interfaces excluded from use by Hadoop may allow other hostnames.)" The "one hostname for all interfaces" only applies to the interfaces used for the Stack. An interface used only by administrators, for example, not for submitting or processing any Hadoop jobs, could assign the server a different hostname on that network. But if you violate #5, you threaten the stability of your configuration. #7 describes precisely the constraints within which you can use /etc/hosts files instead of DNS. It should perhaps mention that, for example, it would work to have the cluster members know their peers only via one network, and know its clients via one other network. The clients would know the cluster members by the second network, with the SAME hostnames, and that's okay. As long as clients and servers have different /etc/host files, and you use hostnames not IP addresses for configurations, it can often work. And as #7 points out, if these constraints don't work for your needs, it isn't that hard to add DNS. The example you gave is a possible result, yes. More broadly, all the servers would share /etc/hosts files in which all the servers are identified with an ip address on the intra-cluster network: namenode2 192.168.1.2 datanode3 192.168.1.3 datanode4 192.168.1.4 datanode5 192.168.1.5 # etc. while the clients would share /etc/hosts files in which those servers are identified with an ip address on the client/server access network: namenode2 10.0.9.2 datanode3 10.0.9.3 datanode4 10.0.9.4 datanode5 10.0.9.5 # etc. And this is a good example of why you MUST use hostnames not ip addresses, and dfs.client.use.datanode.hostname must be set true, because the clients and servers must be able to specify cluster servers to each other, and the ip addresses won't be mutually accessible. ... View more

Hi @Arsalan Siddiqi , yes, this is what I expected for Putty. By using the Putty configuration dialogue as shown in your screenshot, you ALREADY have an ssh connection to the guest VM, so typing `ssh` in the Putty terminal window is unnecessary and `scp` won't work -- instead of asking the host OS to talk to the guest OS, you would be asking the guest OS to talk to itself! I'm glad `pscp` (in a cmd window) worked for you -- and thanks for accepting the answer. Your last question, "why when i connect via Putty to root@127.0.0.1 p 2222 in windows and do a "dir" command, i see different folders as to when i do a DIR in virtualbox terminal??" is a puzzler. I would suggest, in each window (Putty terminal and Virtualbox terminal), you type the linux command `pwd`, which stands for "print working directory". It may be that the filesystem location you are ending up at login in the two tools is different. Also you can try the linux `ls` command. ... View more

(Got this wrong the first time! The following should be correct.) BTW, if your clusters have been inadvertently configured for Federation, and you want to disentangle them, it can be a little tricky. You can't just suddenly change all their configurations, because that would cause the namenodes to lose track of blocks on the remote datanodes. The needed process is too complex to detail here, but basically you need to do something like this: The test for whether this problem exists is to open each NameNode's web UI ( http://<NameNode_FQDN>:50070 ) and navigate to the DataNodes page, then see if datanodes from the OTHER cluster are shown. If so, the two clusters have Federated. To separate them, first, change the configuration of each Namenode so that it stops serving namespaces from both clusters. I believe it would be best to reconfigure both Namenodes in each HA grouping at the same time, so a brief downtime will be needed. Then decommission each of the datanodes individually, from all clusters/namenodes, a few at a time (where "few" means a small percentage of the overall cluster size), so they flush their blocks to other nodes. Then, after each datanode is no longer referenced by any namespace, you change ITS configuration to only register to the Namenodes/Nameservices on its own cluster. Restart it, join it to its cluster, and continue with the next datanode. Alternate between the datanodes on the two clusters, so you don't run into storage space issues. Continue until all the datanodes have been so "gently" reconfigured to a single cluster's namespace(s). At the end of the process, all the nodes in each cluster will have a non-Federated config. This can be done without data loss and only brief NN downtime, but it will require significant time commitment, depending on the cluster sizes. Please don't try this unless you thoroughly understand the issues involved. If you have any doubts, consulting assistance may be appropriate. ... View more