I am fairly new to Cassandra and currently have to following table in Cassandra:
CREATE TABLE time_data (
id int,
secondary_id int,
timestamp timestamp,
value bigint,
PRIMARY KEY ((id, secondary_id), timestamp)
);
The compound partition key (with secondary_id) is necessary in order to not violate max partition sizes.
The issue I am running in to is that I would like to complete the query SELECT * FROM time_data WHERE id = ?. Because the table has a compound partition key, this query requires filtering. I realize this is a querying a lot of data and partitions, but it is necessary for the application. For reference, id has relatively low cardinality and secondary_id has high cardinality.
What is the best way around this? Should I simply allow filtering on the query? Or is it better to create a secondary index like CREATE INDEX id_idx ON time_data (id)?
You will need to specify full partition key on queries (ALLOW FILTERING will impact performance badly in most cases).
One way to go could be if you know all secondary_id (you could add a table to track them in necessary) and do the job in your application and query all (id, secondary_id) pairs and process them afterwards. This has the disadvantage of beeing more complex but the advantage that it can be done with async queries and in parallel so many nodes in your cluster participate in processing your task.
See also https://www.datastax.com/dev/blog/java-driver-async-queries
Related
I'm debugging an issue and the logs should be sitting on a time range between 4/23/19~ 4/25/19
There are hundreds of millions of records on our production.
It's impossible to locate the target records using random sort.
Is there any workaround to search in a time range without partition key?
select * from XXXX.report_summary order by modified_at desc
Schema
...
"modified_at" "TimestampType" "regular"
"record_end_date" "TimestampType" "regular"
"record_entity_type" "UTF8Type" "clustering_key"
"record_frequency" "UTF8Type" "regular"
"record_id" "UUIDType" "partition_key"
First, ORDER BY is really quite superfluous in Cassandra. It can only operate on your clustering columns within a partition, and then only on the exact order of the clustering columns. The reason for this, is that Cassandra reads sequentially from the disk, so it writes all data according to the defined clustering order to begin with.
So IMO, ORDER BY in Cassandra is pretty useless, except for cases where you want to change the sort direction (ascending/descending).
Secondly, due to its distributed nature, you need to take a query-oriented approach to data modeling. In other words, your tables must be designed to support the queries you intend to run. Now you can find ways around this, but then you're basically doing a full table scan on a distributed cluster, which won't end well for anyone.
Therefore, the recommended way to go about that, would be to build a table like this:
CREATE TABLE stackoverflow.report_summary_by_month (
record_id uuid,
record_entity_type text,
modified_at timestamp,
month_bucket bigint,
record_end_date timestamp,
record_frequency text,
PRIMARY KEY (month_bucket, modified_at, record_id)
) WITH CLUSTERING ORDER BY (modified_at DESC, record_id ASC);
Then, this query will work:
SELECT * FROM report_summary_by_month
WHERE month_bucket = 201904
AND modified_at >= '2019-04-23' AND modified_at < '2019-04-26';
The idea here, is that as you care about the order of the results, you need to partition by something else to allow for sorting to work. For this example, I picked month, hence I've "bucketed" your results by month into a partition key called month_bucket. Within each month, I'm clustering on modified_at in DESCending order. This way, the most-recent results are at the "top" of the partition. Then, I threw in record_id as a tie-breaker key to help ensure uniqueness.
If you're still focused on doing this the wrong way:
You can actually run a range query on your current schema. But with "hundreds of millions of records" across several nodes, I don't have high hopes for that to work. But you can do it with the ALLOW FILTERING directive (which you shouldn't ever really use).
SELECT * FROM report_summary
WHERE modified_at >= '2019-04-23'
AND modified_at < '2019-04-26' ALLOW FILTERING;
This approach has the following caveats:
With many records across many nodes, it will likely time out.
Without being able to identify a single partition for this query, a coordinator node will be chosen, and that node has a high chance of becoming overloaded.
As this is pulling rows from multiple partitions, a sort order cannot be enforced.
ALLOW FILTERING makes Cassandra work in ways that it really wasn't designed to, so I would never use that on a production system.
If you really need to run a query like this, I recommend using an in-memory aggregation tool, like Spark.
Also, as the original question was about ORDER BY, I wrote an article a while back which better explains this topic: https://www.datastax.com/dev/blog/we-shall-have-order
I have a high-write table I'm moving from Oracle to Cassandra. In Oracle the PK is a (int: clientId, id: UUID). There are about 10 billion rows. Right off the bat I run into this nonsensical warning:
https://docs.datastax.com/en/cql/3.3/cql/cql_using/useWhenIndex.html :
"If you create an index on a high-cardinality column, which has many distinct values, a query between the fields will incur many seeks for very few results. In the table with a billion songs, looking up songs by writer (a value that is typically unique for each song) instead of by their artist, is likely to be very inefficient. It would probably be more efficient to manually maintain the table as a form of an index instead of using the Cassandra built-in index."
Not only does this seem to defeat efficient find by PK it fails to define what it means to "query between the fields" and what the difference is between a built-in index, a secondary-index, and the primary_key+clustering subphrases in a create table command. A junk description. This is 2019. Shouldn't this be fixed by now?
AFAIK it's misleading anyway:
CREATE TABLE dev.record (
clientid int,
id uuid,
version int,
payload text,
PRIMARY KEY (clientid, id, version)
) WITH CLUSTERING ORDER BY (id ASC, version DESC)
insert into record (id,version,clientid,payload) values
(d5ca94dd-1001-4c51-9854-554256a5b9f9,3,1001,'');
insert into record (id,version,clientid,payload) values
(d5ca94dd-1002-4c51-9854-554256a5b9e5,0,1002,'');
The token on clientid indeed shows they're in different partitions as expected.
Turning to the big point. If one was looking for a single row given the clientId, and UUID ---AND--- Cassandra allowed you to skip specifying the clientId so it wouldn't know which node(s) to search, then sure that find could be slow. But it doesn't:
select * from record where id=
d5ca94dd-1002-4c51-9854-554256a5b9e5;
InvalidRequest: ... despite the performance unpredictability,
use ALLOW FILTERING"
And ditto with other variations that exclude clientid. So shouldn't we conclude Cassandra handles high cardinality tables searches that return "very few results" just fine?
Anything that requires reading the entire context of the database wont work which is the case with scanning on id since any of your clientid partition key's may contain one. Walking through potentially thousands of sstables per host and walking through each partition of each of those to check will not work. If having hard time with data model and not totally getting difference between partition keys and clustering keys I would recommend you walk through some introduction classes (ie datastax academy), youtube videos or book etc before designing your schema. This is not a relational database and designing around your data instead of your queries will get you into trouble. When moving from oracle you should not just copy your tables over and move the data or it will not work as well.
The clustering key is the order in which the data for a partition is ordered on disk which is what it is referring to as "build-in index". Each sstable has an index component that contains the partition key locations for that sstable. This also includes an index of the clustering keys for each partition every 64kb (by default at least) that can be searched on. The clustering keys that exist between each of these indexed points are unknown so they all have to be checked. A long time ago there was a bloom filter of clustering keys kept as well but it was such a rare use case where it helped vs the overhead that it was removed in 2.0.
Secondary indexes are difficult to scale well which is where the warning comes from about cardinality, I would strongly recommend just denormalizing data and not using index in any form as using large scatter gather queries across a distributed system is going to have availability and performance issues. If you really need it check out http://www.doanduyhai.com/blog/?p=13191 to try to get the data right (not worth it in my opinion).
Using Cassandra as db:
Say we have this schema
primary_key((id1),id2,type) with index on type, because we want to query by id1 and id2.
Does query like
SELECT * FROM my_table WHERE id1=xxx AND type='some type'
going to perform well?
I wonder if we have to create and manage another table for this situation?
The way you are planning to use secondary index is ideal (which is rare). Here is why:
you specify the partition key (id1) in your query. This ensures that
only the relevant partition (node) will be queried, instead of
hitting all the nodes in the cluster (which is not scalable)
You are (presumably) indexing an attribute of low cardinality (I can imagine you have maybe a few hundred types?), which is the sweet spot when using secondary indexes.
Overall, your data model should perform well and scale. Yet, if you look for optimal performances, I would suggest you use an additional table ((id1), type, id2).
Finale note: if you have a limited number of type, you might consider using solely ((id1), type, id2) as a single table. When querying by id1-id2, just issue a few parallel queries against the possible value of type.
The final decision needs to take into account your target latency, the disk usage (duplicating table with a different primary key is sometimes too expensive), and the frequency of each of your queries.
I have a table of events and would like to extract the first timestamp (column unixtime) for each user.
Is there a way to do this with a single Cassandra query?
The schema is the following:
CREATE TABLE events (
id VARCHAR,
unixtime bigint,
u bigint,
type VARCHAR,
payload map<text, text>,
PRIMARY KEY(id)
);
CREATE INDEX events_u
ON events (u);
CREATE INDEX events_unixtime
ON events (unixtime);
CREATE INDEX events_type
ON events (type);
According to your schema, each user will have a single time stamp. If you want one event per entry, consider:
PRIMARY KEY (id, unixtime).
Assuming that is your schema, the entries for a user will be stored in ascending unixtime order. Be careful though...if it's an unbounded event stream and users have lots of events, the partition for the id will grow and grow. It's recommended to keep partition sizes to tens or hundreds of megs. If you anticipate larger, you'll need to start some form of bucketing.
Now, on to your query. In a word, no. If you don't hit a partition (by specifying the partition key), your query becomes a cluster wide operation. With little data it'll work. But with lots of data, you'll get timeouts. If you do have the data in its current form, then I recommend you use the Cassandra Spark connector and Apache Spark to do your query. An added benefit of the spark connectory is that if you have cassandra nodes as spark worker nodes, due to locality, you can efficiently hit a secondary index without specifying the partition key (which would normally cause a cluster wide query with timeout issues, etc.). You could even use Spark to get the required data and store it into another cassandra table for fast querying.
Please note that I am first time using NoSQL and pretty much every concept is new in this NoSQL world, being from RDBMS for long time!!
In one of my heavy used applications, I want to use NoSQL for some part of the data and move out from MySQL where transactions/Relational model doesn't make sense. What I would get is, CAP [Availability and Partition Tolerance].
The present data model is simple as this
ID (integer) | ENTITY_ID (integer) | ENTITY_TYPE (String) | ENTITY_DATA (Text) | CREATED_ON (Date) | VERSION (interger)|
We can safely assume that this part of application is similar to Logging of the Activity!
I would like to move this to NoSQL as per my requirements and separate from Performance Oriented MySQL DB.
Cassandra says, everything in it is simple Map<Key,Value> type! Thinking in terms of Map level,
I can use ENTITY_ID|ENTITY_TYPE|ENTITY_APP as key and store the rest of the data in values!
After reading through User Defined Types in Cassandra, can I use UserDefinedType as value which essentially leverage as One Key and multiple values! Otherwise, Use it as normal column level without UserDefinedType! One idea is to use the same model for different applications across systems where it would be simple logging/activity data can be pushed to the same, since the key varies from application to application and within application each entity will be unique!
No application/business function to access this data without Key, or in simple terms no requirement to get data randomly!
References: http://www.ebaytechblog.com/2012/07/16/cassandra-data-modeling-best-practices-part-1/
Let me explain the cassandra data model a bit (or at least, a part of it). You create tables like so:
create table event(
id uuid,
timestamp timeuuid,
some_column text,
some_column2 list<text>,
some_column3 map<text, text>,
some_column4 map<text, text>,
primary key (id, timestamp .... );
Note the primary key. There's multiple columns specified. The first column is the partition key. All "rows" in a partition are stored together. Inside a partition, data is ordered by the second, then third, then fourth... keys in the primary key. These are called clustering keys. To query, you almost always hit a partition (by specifying equality in the where clause). Any further filters in your query are then done on the selected partition. If you don't specify a partition key, you make a cluster wide query, which may be slow or most likely, time out. After hitting the partition, you can filter with matches on subsequent keys in order, with a range query on the last clustering key specified in your query. Anyway, that's all about querying.
In terms of structure, you have a few column types. Some primitives like text, int, etc., but also three collections - sets, lists and maps. Yes, maps. UDTs are typically more useful when used in collections. e.g. A Person may have a map of addresses: map. You would typically store info in columns if you needed to query on it, or index on it, or you know each row will have those columns. You're also free to use a map column which would let you store "arbitrary" key-value data; which is what it seems you're looking to do.
One thing to watch out for... your primary key is unique per records. If you do another insert with the same pk, you won't get an error, it'll simply overwrite the existing data. Everything in cassandra is an upsert. And you won't be able to change the value of any column that's in the primary key for any row.
You mentioned querying is not a factor. However, if you do find yourself needing to do aggregations, you should check out Apache Spark, which works very well with Cassandra (and also supports relational data sources....so you should be able to aggregate data across mysql and cassandra for analytics).
Lastly, if your data is time series log data, cassandra is a very very good choice.