when using jupyter to cache some data into spark (using sqlcontext.cacheTable) i can see the table cached for the sparkcontext running within Jupyter. But now i want to access those cached tables from BI tools via odbc using the thrift server. when checking the thriftserver cache I dont see any table, the question is how do i get those tables cached to be consumed from BI tools?
do i have to send the same spark commands via jdbc? in that case, is the context related to the current session?
regards,
miguel
I found the solution. In order to have the tables cached to be used with jdbc/odbc clients via thriftserver i have to use CACHE TABLE from one of the clients, for exmaple from beeline. Once this is done the table is in-memory for all the different sessions.
It is also important to be sure you are using the right spark thriftserver. In order to know that just do a show table; in beeline, if you get just one column back you are not using the spark one and the CACHE TABLE wont work.
Related
Is Hive and Impala integration possible?
After data processing in hive i want to store result data in impala for better read, is it possible?
If yes can you please share one example.
Both hive and impala, do not store any data. The data is stored in the HDFS location and hive an impala both are used just to visualize/transform the data present in the HDFS.
So yes, you can process the data using hive and then read it using impala, considering both of them have been setup properly. But since impala needs to be refreshed, you need to run the invalidate metadata and refresh commands
Impala uses the HIVE metastore to read the data. Once you have a table created in hive, it is possible to read the same and query the same using Impala. All you need is to refresh the table or trigger INVALIDATE METADATA in impala to read the data.
Hope this helps :)
Hive and impala are two different query engines. Each query engine is unique in terms of its architecture as well as performance. We can use hive metastore to get metadata and running query using impala. The common usecase is to connect impala/hive from tableau. If we are visualizing hive from tableau, we can get the latest data without any work around. If we keep on loading the data continuously, metadata will be updated as well. Impala does not aware of those changes. So we should run metadata invalidate query by connecting impalad to refresh its state and sync with the latest info available in metastore. So that user will get the same results as hive when the run the same query from tableau using impala engine.
There is no configuration parameter available now to run this invalidation query periodically. This blog reads well to execute meta data invalidation query through oozie scheduler periodically to handle such problems, Or simply we can set up a cronjob from the server itself.
I am trying to query GitHub data provided by ghtorrent API using hadoop. how can I inject this much data(4-5 TB) into HDFS? Also, their databases are real time. Is it possible to process real time data in hadoop using tools such as pig, hive, hbase?
Go through this presentation . It has described the way you can connect to their MySql or MongoDb instance and fetch data. Basically you have to share your public key, they will add that key to their repository and then you can ssh. As an alternative you can download their periodic dumps from this link
Imp Link :
query mongodb programatically
connect to mysql instance
For processing real time data, you cannt do that uisng Pig, Hive. Those are Batch processing tools. Consider using Apache Spark.
As has been discussed in impala tutorials, Impala uses a Metastore shared by Hive. but has been mentioned that if you create or do some editions on tables using hive, you should execute INVALIDATE METADATA or REFRESH command to inform impala about changes.
So I've got confused and my question is: if the Database of Metadata is shared, why there is a need for executing INVALIDATE METADATA or REFRESH by impala?
and if it is for caching of metadata by impala, why the daemons do not update their cache in the occurrence of cache miss themselves and without need to refresh metadata manually?
any help is appreciated.
Ok! Let's start with your question in the comment that what is the benefit of a centralized meta store.
Having a central meta store don't require the user to maintain meta data at two different locations, one each for Hive and Impala. User can have a central repository and both the tools can access this location for any metadata information.
Now, the second part, why there is a need to do INVALIDATE METADATA or REFRESH when the meta store is shared?
Impala utilizes Massively Parallel Processing paradigm to get the work done. Instead of reading from the centralized meta store for each and every query, it tends to keep the metadata with executor nodes so that it can completely bypass the COLD STARTS where a significant amount of time may be spent in reading the metadata.
INVALIDATE METADATA/REFRESH propagates the metadata/block information to the executor nodes.
Why do it manually?
In the earlier version of Impala, catalogd process was not present. The meta data updates were need to be propagated via the aforementioned commands. Starting Impala 1.2, catalogd is added and this process relays the metadata changes from Impala SQL statements to all the nodes in a cluster.
Hence removing the need to do it manually!
Hope that helps.
It is shared, but Impala caches the metadata and uses its statistics in its optimizer, but if it's changed in hive, you have to manually tell impala to refresh its cache, which is kind of inconvenient.
But if you create/change tables in impala, you don't have to do anything on the hive side.
#masoumeh when you modify a table via Impala SQL statements no need for INVALIDATE METADATA or REFRESH, this job is done by catalogd.
But when you insert :
a NEW table through HIVE i.e sqoop import .... --hive-import ... then you have to do : INVALIDATE METADATA tableName via Impala-Shell.
new data files into an existing table (append data) then you have to : REFRESH tableName because the only thing you want is the metadata for the last added info.
I am new to hadoop. Please help me in below concept.
It is always good practice to use hive metastore(into other db like mysql etc) for production purpose.
What is the exact role and need of storing meatadata on RDBMS ?
If we create a client application to get hive data on UI, will this metadata store help to improve the performance to get data?
If yes What will be the architecture of this kind of client application? Will it hit first RDBMS metastore ? How it will be different form querying hive directly in some other way like using thrift?
Hadoop experts ,please help
Thanks
You can use prestodb that allows you to run/translate SQL queries against HIVE. It has a mysql connector that you can use to exploit your stored hive schema.
Thus from your client application, you just need a JDBC driver as any RDBMS out there.
I'm trying to create tables pragmatically using JDBC. However, I can't really see the table I created from the hive shell. What's worse, when i access hive shell from different directories, i see different result of the database.
Is any setting i need to configure?
Thanks in advance.
Make sure you run hive from the same directory every time because when you launch hive CLI for the first time, it creates a metastore derby db in the current directory. This derby DB contains metadata of hive tables. If you change directories, you will have unorganized metadata for hive tables. Also the Derby DB cannot handle multiple sessions. To allow for concurrent Hive access you would need to use a real database to manage the Metastore rather than the wimpy little derbyDB that comes with it. You can download mysql for this and change hive properties for jdbc connection to mysql type 4 pure java driver.
Try emailing the Hive userlist or the IRC channel.
You probably need to setup the central Hive metastore (by default, Derby, but it can be mySQL/Oracle/Postgres). The metastore is the "glue" between Hive and HDFS. It tells Hive where your data files live in HDFS, what type of data they contain, what tables they belong to, etc.
For more information, see http://wiki.apache.org/hadoop/HiveDerbyServerMode
Examine your hadoop logs. For me this happened when my hadoop system was not setup properly. The namenode was not able to contact the datanodes on other machines etc.
Yeah, it's due to the metastore not being set up properly. Metastore stores the metadata associated with your Hive table (e.g. the table name, table location, column names, column types, bucketing/sorting information, partitioning information, SerDe information, etc.).
The default metastore is an embedded Derby database which can only be used by one client at any given time. This is obviously not good enough for most practical purposes. You, like most users, should configure your Hive installation to use a different metastore. MySQL seems to be a popular choice. I have used this link from Cloudera's website to successfully configure my MySQL metastore.