In my Beam workflow, I fetch daily data from an API endpoint into my database and while I am doing that, I am joining additional info from a fact table onto the daily data using CoGroupByKey.
Now, if the key in my primary table is not matching with the fact table, i.e. I see daily data for a new object, how do I get a list/PCollection of all these non-matching keys so I can fetch the new "facts" for the non-matching keys?
In the out of the CoGroupByKey each element is a CoGbkResult.
In the CoGbkResult for each key there will be an iterable for elements from the primary table and and iterable for elements from the fact table. If the iterable from the fact table is empty, then there were no matches.
You may find that using Beam schemas and the CoGroup transform is even easier.
Related
Any way to do this? Basically trying to do a SQL UPDATE SET function if matching record for one or more key fields exists in another dataset.
Tried using Joins and Merge. Joins seems like more steps and the Merge appends records instead of updating the correlating rows.
I am new to PostgreSql world. We chose this DB so that we could query our JSON results for filter queries like contains, less than , greater than, etc. JSON results are dynamic and we cannot know in advance what keys will be generated as the output. Table (result_id (int64),jsondata(jsonb)) data looks like this
id1,{k1:vab,k2:abc,k3:def}
id1,{k1:abv,k2:v7,k3:ghu}
id1,{k1:v5,k2:vdd,k3:vew}
id1,{k1:v6,k2:v9s,k3:ved}
id2,{k4:vw,k5:vds,k6:vdss}
id2,{k4:v1,k5:fgg,k6:dd}
id2,{k4:qw,k5:gfd,k6:ess}
id2,{k4:er,k5:dfs,k6:fss}
My queries would be something like
Select * from table where result_id = 'id1' and jsondata->'k1' contains 'ab'
My script outputs a json content that I store in this table.
Each json key is represented in a Grid column and json key's values are column data.Grid offers filtering capabilities, which means filtering on JSON data.
My problem is that the filtering can happen on any JSON key, but key names are not static. Keys (json output) might change when the script content is changed So previously indexed keys would become irrelevant. But if the script is not changed the keys remain constant.
How do I apply indexing so that my JSON filter operations become faster? The same table contains many keys within the same JSON row and across rows. Wouldn't it be inefficient to index all keys so that filtering can be made efficient?
I'm building a simple Scala Play app which stores data in a Cassandra DB using the Phantom DSL driver for Scala. One of the nice features of Cassandra is that you can do partial updates i.e. so long as you provide the key columns, you do not have to provide values for all the other columns in the table. Cassandra will merge the data into your existing record based on the key.
Unfortunately, it seems this doesn't work with Phantom DSL. I have a table with several columns, and I want to be able to do an update, specifying values just for the key and one of the data columns, and let Cassandra merge this into the record as usual, while leaving all the other data columns for that record unchanged.
But Phantom DSL overwrites existing columns with null if you don't specify values in your insert/update statement.
Does anybody know of a work-around for this? I don't want to have to read/write all the data columns every time, as eventually the data columns will be quite large.
FYI I'm using the same approach to my Phantom coding as in these examples:
https://github.com/thiagoandrade6/cassandra-phantom/blob/master/src/main/scala/com/cassandra/phantom/modeling/model/GenericSongsModel.scala
It would be great to see some code, but partial updates are possible with phantom. Phantom is an immutable builder, it will not override anything with null by default. If you don't specify a value it won't do anything about it.
database.table.update.where(_.id eqs id).update(_.bla setTo "newValue")
will produce a query where only the values you've explicitly set to something will be set to null. Please provide some code examples, your problem seems really strange as queries don't keep track of table columns to automatically add in what's missing.
Update
If you would like to delete column values, e.g set them to null inside Cassandra basically, phantom offers a different syntax which does the same thing:
database.table.delete(_.col1, _.col2).where(_.id eqs id)`
Furthermore, you can even delete map entries in the same fashion:
database.table.delete(_.props("test"), _.props("test2").where(_.id eqs id)
This assumes props is a MapColumn[Table, Record, String, _], as the props.apply(key: T) is typesafe, so it will respect the keytype you define for the map column.
I do have multiple tables (MySQL) and I want to have a single index for them.
Each table has the primary key of int autoincrement type.
The structure of collected data is the same for each table (so no conflict), but as the IDs collide so it seems that I have to query each index separately (unless you can give me a hint of how to avoid ID collision)
Question is: If I query each index separately does it means that the weight of returned results are comparable between indexes?
unless you can give me a hint of how to avoid ID collision
See for example
http://sphinxsearch.com/forum/view.html?id=13078
You can just arrange for the ids to be offset differently. The 'sphinx document id' doesnt have to match the real primary key, but having a simple mapping makes the application simpler.
You have a choice between one-index, one-source (using a single sql query to union all the tables together. one-index, many-source. (a source per table, all making one index) or many-indexes (one index per table, each with own source). Which ever way will give the same query results.
If I query each index separately does it means that the weight of returned results are comparable between indexes?
Pretty much. The difference should be negiblibe that doesnt matter whic way round you do it.
I have a Cassandra ColumnFamily (0.6.4) that will have new entries from users. I'd like to query Cassandra for those new entries so that I can process that data in another system.
My sense was that I could use a TimeUUIDType as the key for my entry, and then query on a KeyRange that starts either with "" as the startKey, or whatever the lastStartKey was. Is this the correct method?
How does get_range_slice actually create a range? Doesn't it have to know the data type of the key? There's no declaration of the data type of the key anywhere. In the storage_conf.xml file, you declare the type of the columns, but not of the keys. Is the key assumed to be of the same type as the columns? Or does it do some magic sniffing to guess?
I've also seen reference implementations where people store TimeUUIDType in columns. However, this seems to have scale issues as this particular key would then become "hot" since every change would have to update it.
Any pointers in this case would be appreciated.
When sorting data only the column-keys are important. The data stored is of no consequence neither is the auto-generated timestamp. The CompareWith attribute is important here. If you set CompareWith as UTF8Type then the keys will be interpreted as UTF8Types. If you set the CompareWith as TimeUUIDType then the keys are automatically interpreted as timestamps. You do not have to specify the data type. Look at the SlicePredicate and SliceRange definitions on this page http://wiki.apache.org/cassandra/API This is a good place to start. Also, you might find this article useful http://www.sodeso.nl/?p=80 In the third part or so he talks about slice ranging his queries and so on.
Doug,
Writing to a single column family can sometimes create a hot spot if you are using an Order-Preserving Partitioner, but not if you are using the default Random Partitioner (unless a subset of users create vastly more data than all other users!).
If you sorted your rows by time (using an Order-Preserving Partitioner) then you are probably even more likely to create hotspots, since you will be adding rows sequentially and a single node will be responsible for each range of the keyspace.
Columns and Keys can be of any type, since the row key is just the first column.
Virtually, the cluster is a circular hash key ring, and keys get hashed by the partitioner to get distributed around the cluster.
Beware of using dates as row keys however, since even the randomization of the default randompartitioner is limited and you could end up cluttering your data.
What's more, if that date is changing, you would have to delete the previous row since you can only do inserts in C*.
Here is what we know :
A slice range is a range of columns in a row with a start value and an end value, this is used mostly for wide rows as columns are ordered. Known column names defined in the CF are indexed however so they can be retrieved specifying names.
A key slice, is a key associated with the sliced column range as returned by Cassandra
The equivalent of a where clause uses secondary indexes, you may use inequality operators there, however there must be at least ONE equals clause in your statement (also see https://issues.apache.org/jira/browse/CASSANDRA-1599).
Using a key range is ineffective with a Random Partitionner as the MD5 hash of your key doesn't keep lexical ordering.
What you want to use is a Column Family based index using a Wide Row :
CompositeType(TimeUUID | UserID)
In order for this not to become hot, add a first meaningful key ("shard key") that would split the data accross nodes such as the user type or the region.
Having more data than necessary in Cassandra is not a problem, it's how it is designed, so what you must ask yourself is "what do I need to query" and then design a Column Family for it rather than trying to fit everything in one CF like you'd do in an RDBMS.