I am developing scripts to collect data from DB2 environments. The db2pd tool has a lot of very cool data, but it is a pain to run and collect data compared to running a SQL. The MON_PKG_CACHE_SUMMARY provides equivalent data to the db2pd -tcbstats.
Does anyone know which DB2 function and/or view can provide equivalent data to db2pd -edus please?
Take a look at the ENV_GET_DB2_EDU_SYSTEM_RESOURCES table function.
Related
We're using Tableau 10.5.6. I used a reporting tool years ago called Oracle Sales Analyzer. In that tool you could get to the queries generated by the reports and graphs you created through back-end catalogs using their command line.
There you could rewrite the query to be more efficient by fine-tuning the code if you needed. It was a very cool feature of that reporting tool for geeks like me who like to dive into the back end of the product and tune it at a very low level.
My question is, does Tableau have any of this type of facility? Is there a way to get to the queries that get stored once you create a report or a graph. Also is there command line where you can access these catalogs if they exist? Otherwise are these queries just stored in ASCII flat files that can be accessed by a user.
Thanks!
There are two ways that Tableau will query a database.
Option 1: Custom SQL
In your data source, you paste in the sql you have written and Tableau will pass that query through to the database. This gives you complete control over the sql, including adding any indexing hints you may want. See https://onlinehelp.tableau.com/current/pro/desktop/en-us/customsql.html
Option 2: Use the Tableau data source designer
This is what many people do. Here, you visually design your data source with the joins. Tableau translates that design into what the Hyper engine considers to be the most effective way to run the query. Sometimes, Hyper translates that into a regular sql statement. Sometimes it does some additional things to help boost performance, like breaking it up into different queries. A lot depends on the db engine you are connecting to. There is no "sql" stored in a flat file for this. Tableau just translates your design at run-time. The Hyper engine does a good job with fine-tuning, assuming you have an efficient database design with proper indexing and current table statistics.
There is a way to see the sql from option 2 at run-time using Performance Recording. Performance Recording keeps track of each step of the visualization process and will spit out the sql statement(s) that Tableau ran to generate your dataset. The sql is not stored in the twb file though, it's a run-time analysis.
We have a few collections in mongodb that we wish to transfer to redshift (on an automatic incremental daily basis).
How can we do it? Should we export the mongo to csv?
I wrote some code to export data from Mixpanel into Redshift for a client. Initially the client was exporting to Mongo but we found Redshift offered very large performance improvements for query. So first of all we transferred the data out of Mongo into Redshift, and then we came up with a direct solution that transfers the data from Mixpanel to Redshift.
To store JSON data in Redshift first you need to create a SQL DDL to store the schema in Redshift i.e. a CREATE TABLE script.
You can use a tool like Variety to help as it can give you some insight into your Mongo schema. However it does struggle with big datasets - you might need to subsample your dataset.
Alternatively DDLgenerator can generate DDL from various sources including CSV or JSON. This also struggles with large datasets (well the dataset I was dealing with was 120GB).
So in theory you could use MongoExport to generate CSV or JSON from Mongo and then run it through DDL generator to get a DDL.
In practice I found using JSON export a little easier because you don't need to specify the fields you want to extract. You need to select the JSON array format. Specifically:
mongoexport --db <your db> --collection <your_collection> --jsonArray > data.json
head data.json > sample.json
ddlgenerator postgresql sample.json
Here - because I am using head - I use a sample of the data to show the process works. However, if your database has schema variation, you want to compute the schema based on the whole database which could take several hours.
Next you upload the data into Redshift.
If you have exported JSON, you need to use Redshift's Copy from JSON feature. You need to define a JSONpath to do this.
For more information check out the Snowplow blog - they use JSONpaths to map the JSON on to a relational schema. See their blog post about why people might want to read JSON to Redshift.
Turning the JSON into columns allows much faster query than the other approaches such as using JSON EXTRACT PATH TEXT.
For incremental backups, it depends if data is being added or data is changing. For analytics, it's normally the former. The approach I used is to export the analytic data once a day, then copy it into Redshift in an incremental fashion.
Here are some related resources although in the end I did not use them:
Spotify has a open-source project called Luigi - this code claims to upload JSON to Redshift but I haven't used it so I don't know if it works.
Amiato have a web page that says they offer a commercial solution for loading JSON data into Redshift - but there is not much information beyond that.
This blog post discusses performing ETL on JSON datasources such as Mixpanel into Redshift.
Related Redit question
Blog post about dealing with JSON arrays in Redshift
Honestly, I'd recommend using a third party here. I've used Panoply (panoply.io) and would recommend it. It'll take your mongo collections and flatten them into their own tables in redshift.
AWS Database Migration Service(DMS) Adds Support for MongoDB and Amazon DynamoDB.So I think now onward best option to migrate from MongoDB to Redshift is DMS.
MongoDB versions 2.6.x and 3.x as a database source
Document Mode and Table Mode supported
Supports change data capture(CDC)
Details - http://docs.aws.amazon.com/dms/latest/userguide/CHAP_Source.MongoDB.html
A few questions that would be helpful to know would be:
Is this an add-only always increasing incremental sync i.e. data is only being added and not being updated / removed or rather your redshift instance is interested only in additions?
Is the data inconsistency due to delete / updates happening at source and not being fed to redshift instance ok?
Does it need to be daily-incremental batch or can it be realtime as it is happening as well?
Depending on your situation may be mongoexport works for you, but you have to understand the shortcoming of it, which can be found at http://docs.mongodb.org/manual/reference/program/mongoexport/ .
I had to tackle the same issue (not on a daily basis though).
as ask mentioned, You can use mongoexport in order to export the data, but keep in mind that redshift doesn't support arrays, so in case your collections data contains arrays you'll find it a bit problematic.
My solution to this was to pipe the mongoexport into a small utility program I wrote that transforms the mongoexport json rows into my desired csv output.
piping the output also allows you to make the process parallel.
Mongoexport allows you to add a mongodb query to the command, so if your collection data supports it you can spawn N different mongoexport processes, pipe it's results into the other program and decrease the total runtime of the migration process.
Later on, I uploaded the files to S3, and performed a COPY into the relevant table.
This should be a pretty easy solution.
Stitch Data is the best tool ever I've ever seen to replicate incrementally from MongoDB to Redshift within a few clicks and minutes.
Automatically and dynamically Detect DML, DDL for tables for replication.
As part of some requirement, I need to migrate a schema from some existing database to a new schema in a different database. Some part of it is already done and now I need to compare the 2 schema and make changes in the new schema as per gap finding.
I am not using a tool and was trying to understand some details using syscat command but could not get much success.
Any pointer on what is the best way to solve this?
Regards,
Ramakant
A tool really is the best way to solve this – IBM Data Studio is free and can compare schemas between databases.
Assuming you are using DB2 for Linux/UNIX/Windows, you can do a rudimentary compare by looking at selected columns in SYSCAT.TABLES and SYSCAT.COLUMNS (for table definitions), and SYSCAT.INDEXES (for indexes). Exporting this data to files and using diff may be the easiest method. However, doing this for more complex structures (tables with range or database partitioning, foreign keys, etc) will become very complex very quickly as this information is spread across a lot of different system catalog tables.
An alternative method would be to extract DDL using the db2look utility. However, you can't specify the order that db2look outputs objects (db2look extracts DDL based on the objects' CREATE_TIME), so you can't extract DDL for an entire schema into a file and expect to use diff to compare. You would need to extract DDL into a separate file for each table.
Use SchemaCrawler for IBM DB2, a free open-source tool that is designed to produce text output that is designed to be diffed. You can get very detailed information about your schema, including view and stored procedure definitions. All of the information that you need will be output in a single file, and can be compared very easily using a standard diff tool.
Sualeh Fatehi, SchemaCrawler
unfortunately as per company policy, cannot use these tools at this point of time. So am writing some program using JDBC to get the details and do some comparison kind of stuff.
I am working on a project where I need to programmatically validate and/or compare a database schema between product releases.
I am using Perl and am looking for a cross-platform method to collect the database schema. I am currently able to perform database queries by utilizing the dbisql.exe command and then parsing the results.
I am wondering if there is potentially a stored procedure or set of queries that I can run to collect the database schema.
It appears that the dbunload.exe command could be used to generate a SQL regeneration script however I am thinking that this output may be difficult to parse.
Any feedback would be greatly appreciated.
If you would like to retrieve the DB schema data on a really low level you could query the corresponding system tables. They are in the SYS-Namespace, especially SYSTABLE (for all tables) and SYSCOLUMN for all fields in those tables.
Check the ASA SQL Reference Handbook for the schema of those system tables.
With Perl's DBI you can easily fire queries on those tables. But you will have to create some local storage for the schema to compare the query results with.
Sybase Central v3.0 has the possibility to export DDL with all DB objects;
and I think SC v6.0 can't connect to ASA 11 :(
Dear all ,
Can any one suggest me the postgres tool for linux which is used to find the
difference between the 2 given database
I tried with the apgdiff 2.3 but it gives the difference in terms of schema not the data
but I need both !
Thanks in advance !
Comparing data is not easy especially if your database is huge. I created Python program that can dump PostgreSQL data schema to file that can be easily compared via 3rd party diff programm: http://code.activestate.com/recipes/576557-dump-postgresql-db-schema-to-text/?in=user-186902
I think that this program can be extended by dumping all tables data into separate CSV files, similar to those used by PostgreSQL COPY command. Remember to add the same ORDER BY in SELECT ... queries. I have created tool that reads SELECT statements from file and saves results in separate files. This way I can manage which tables and fields I want to compare (not all fields can be used in ORDER BY, and not all are important for me). Such configuration can be easily created using "dump schema" utility.
Check out dbsolo DBSOLO. It does both object and data compares and can create a sync script based on the results. It's free to try and $99 to buy. My guess is the 99 bucks will be money well spent to avoid trying to come up with your own software to do this.
Data Compare
http://www.dbsolo.com/help/datacomp.html
Object Compare
http://www.dbsolo.com/help/compare.html
apgdiff https://www.apgdiff.com/
It's an opensource solution. I used it before for checking differences between differences in dumps. Quite useful
[EDIT]
It's for differenting by schema only