Mapr Download

0 views

Skip to first unread message

Slikk Huisenga

unread,

Aug 3, 2024, 3:34:45 PM8/3/24

to graninolis

However, I will also caution that currently the logging done by the init script is sparse and not necessarily user friendly to read. If you can't discern the cause from the contents of the wardeninit.log you can post them here and maybe I can help.

Another thing you can do is edit /etc/init.d/mapr-warden and add "set -x" towards the top of the file, right before the "BASEMAPR=" line, then try starting warden again and you'll get a bunch of shell debugging output on your screen. If you copy and paste that output here that should be enough to tell the root cause of the problem.

Even when using MapR in a single node configure.sh is still required. In fact, without configure.sh warden, zookeeper, cldb and other MapR components will lack their configuration and in many cases will fail to start.

I have installed HPE Data Fabric 7.0 sandbox in a Docker container (I edited the container to expose port 7443 in addition to others). I have installed mapr-client software on a separate VM (both VMs are in GCP cloud). I successfully authorised from the client machine using maprlogin password command and see the ticket in /tmp on the client machine:

Now, I don't really need GCP's hadoop command, but I'd like the "native" one to work. How can I do some diagnostics to check that my mapr-client is configured correctly? @ldarby any comment? thank you very much in advance?

Since the stack trace from the google hadoop shows it picked up the MapR client libraries (I believe that's necessary for it to connect at all) then possibly it could try to connect to port 7223 as well and was just chance that it didn't.

@rbukarev About the stack trace, it mentions the com.google.common java libraries, there is a copy of these shipped in the mapr-client package (in /opt/mapr/lib/guava...jar, which MapR hadoop uses), so possibly your system has a separate copy of this library and the Google hadoop has picked up that version via the classpath, and maybe that version isn't compatible with the MapR client library (which Google hadoop is using)?

In YARN Deployment mode, Dremio integrates with YARN ResourceManager to secure compute resourcesin a shared multi-tenant environment. The integration enables enterprises to more easily deploy Dremioon a Hadoop cluster, including the ability to elastically expand and shrink the execution resources.The following diagram illustrates the high-level deployment architecture of Dremio on a MapR cluster.

Installing Dremio requires MapR administrative privileges.Dremio services running on MapR clusters should be run as the mapr user or a user account using a servicewith an impersonation ticket (see MapR 5.2.x, 6.1.x, or 6.2) and have read privileges for the MapR-FS directories/filesthat will either be queried directly or that map to the Hive Metastore.

The site is secure.
The ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Mechanism-based risk assessment is urged to advance and fully permeate into current safety assessment practices, possibly at early phases of drug safety testing. Toxicogenomics is a promising source of mechanisms-revealing data, but interpretative analysis tools specific for the testing systems (e.g. hepatocytes) are lacking. In this study, we present the TXG-MAPr webtool (available at -mapr.eu/WGCNA_PHH/TGGATEs_PHH/ ), an R-Shiny-based implementation of weighted gene co-expression network analysis (WGCNA) obtained from the Primary Human Hepatocytes (PHH) TG-GATEs dataset. The 398 gene co-expression networks (modules) were annotated with functional information (pathway enrichment, transcription factor) to reveal their mechanistic interpretation. Several well-known stress response pathways were captured in the modules, were perturbed by specific stressors and showed preservation in rat systems (rat primary hepatocytes and rat in vivo liver), with the exception of DNA damage and oxidative stress responses. A subset of 87 well-annotated and preserved modules was used to evaluate mechanisms of toxicity of endoplasmic reticulum (ER) stress and oxidative stress inducers, including cyclosporine A, tunicamycin and acetaminophen. In addition, module responses can be calculated from external datasets obtained with different hepatocyte cells and platforms, including targeted RNA-seq data, therefore, imputing biological responses from a limited gene set. As another application, donors' sensitivity towards tunicamycin was investigated with the TXG-MAPr, identifying higher basal level of intrinsic immune response in donors with pre-existing liver pathology. In conclusion, we demonstrated that gene co-expression analysis coupled to an interactive visualization environment, the TXG-MAPr, is a promising approach to achieve mechanistic relevant, cross-species and cross-platform evaluation of toxicogenomic data.

In this post I demonstrate how to integrate StreamSets with MapR in Docker. This is made possible by the MapR persistent application client container (PACC). The fact that any application can use MapR simply by mapping /opt/mapr through Docker volumes is really powerful! Installing the PACC is a piece of cake, too.

To use StreamSets with MapR, the mapr-client package needs to be installed on the StreamSets host. Alternatively (emphasized because this is important) you can run a separate CentOS Docker container which has the mapr-client package installed, then you can share /opt/mapr as a docker volume with the StreamSets container. I like this approach because the MapR installer (which you can download here) can configure a mapr-client container for me! MapR calls this container the Persistent Application Client Container (PACC).

There have been a few cases where the C library within mapr-streams-python segfaults because of permissions issues. Ensure the dd-agent user has read permission on the ticket file, that the dd-agent user is able to run maprcli commands when the MAPR_TICKETFILE_LOCATION environment variable points to the ticket.

Although MapR-FS is built to handle heavy workloads, you will want to watch for any unexpected changes in throughput that would warrant further investigation. If you observe a sustained dip in reads or writes, you can correlate it with system-level metrics, like I/O wait time, to determine if there is a resource bottleneck. High I/O wait time could indicate a failed disk, which would be brought offline along with other disks in the same storage pool. MapR provides useful instructions on how to recover from a disk failure, such as by removing and replacing disks in the case of a hardware failure.

The log highlighted here shows a node failure, which could affect cluster availability if the volume replication factor falls below the minimum factor needed to prevent data loss. If you have the enforceminreplicationforio parameter set to true, the file system will not accept any writes to its containers as long as the minimum replication factor is not met.

As you scale your cluster, monitoring query throughput can help you determine if your MapR NoSQL database (MapR-DB) is processing queries efficiently. MapR-DB sorts JSON documents by their unique document ID, otherwise known as the primary key of each table. While primary key-based data retrieval is quick, querying with other fields is a slow process as every row of the table needs to be scanned sequentially to find the right match(es).

As your database grows, performing full table scans quickly exhausts CPU and disk resources, causing query performance to suffer. Ideally, the number of rows read and returned should be close to equal since efficient queries avoid examining more rows than necessary to return the data you need.

In the graph above, you can see that far more rows are read from MapR-DB tables (mapr.db.table.read_rows, in green) than returned (mapr.db.table.resp_rows, in purple). To boost query performance, you can create secondary indexes on the most frequently queried fields. Secondary indexes order documents by fields other than the primary key to help optimize certain types of queries. To learn more, see the MapR documentation.

If you rely on MapR Event Store for Apache Kafka (MapR-ES) to deliver real-time data for mission-critical applications, it is crucial that you keep an eye on its performance. While MapR-ES is similar to Kafka in a number of ways, it has been specifically designed to transport large streams of data on the MapR platform, and includes built-in support for automatic load balancing, global data replication, and other features.

Map Annotations created by users via the Insight client or webclient all have thenamespace openmicroscopy.org/omero/client/mapAnnotation, whereas otherMap Annotations created via the OMERO API by other tools should have their own distinctnamespace.

We can configure OMERO.mapr to search for Map Annotations of specified namespace,looking for Values under specifed Keys.For example, seach for values under key Gene Symbol or Gene Identifierand namespace openmicroscopy.org/mapr/gene.

OMERO.web 5.6 or newer.Installing from PypiThis section assumes that an OMERO.web is already installed.NB: Configuration of the settings (see below) is not optionaland is required for the app to work.

Now add a top link of Genes with tooltip Find Gene annotations that will take us to the gene search page. The query_string parameters are added to the URL, with "experimenter": -1specifying that we want to search across all users.

Finally, we can add a map annotation to an Image that is in a Screen -> Plate -> Wellor Project -> Dataset -> Image hierarchy.This code uses the OMERO Python API toadd a map annotation corresponding to the configuration above: