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.
Related
I'm playing with Cloud Spanner and I created an imgur clone with the schema as follows:
CREATE TABLE Images (id STRING(36) NOT NULL, createdAt TIMESTAMP, caption STRING(1024), fileType STRING(10)) PRIMARY KEY (id, createdAt DESC)
The id is a version 4 UUID as the GCP documentation specifies so that I avoid hotspots. The createdAt is a timestamp when an image is first created. I have my PRIMARY KEY defined as (id, createdAt DESC) so that I can more easily query by latest added images.
What I don't understand is what happens if I want to get a single image using only SELECT * FROM Images WHERE id = 'some UUID? Will Spanner still search by key in an efficient way, meaning getting the information from the server that stores the specific key in its key range even though I only specified a part of the primary key?
In your simple example, yes. It will try to come up with an efficient execution plan which may include using an index (automatically created for PKs) even though your predicate is on just 1 of the 2-column composite PK because it is on the 1 column. If your predicate was just "...createdAt= then it will scan the table. It would be far more expensive to find matches for col2 in your composite PK of (col1, col2) than it is to just scan col2.
This assumes there's enough data to matter. For example, if you have 42 rows, it really won't matter how you execute the query or what predicates were provided; the number off I/O requests (often the most expensive part of a query) will be the same.
In general, Spanner tries to pick the index it thinks will be most efficient. The actual physical steps don't work like that but conceptually, it's a reasonable way to think about it.
Whether an index is helpful or not depends on a few things and whether it gets picked or not also has dependencies. Does it have statistics, are the statistics correct/fresh, is it making correct estimates on row counts, etc... Composite indexes/keys are a just a bit more interesting as noted above.
Just make sure you always test with enough data (closely matching your production environment if possible).
I have an application which using cassandra as database. and the row of the table filled by three separate moments (by three inputs) , I have four primary keys in that table and these primary keys not available at all the moment going to insert or update.
the error is:
Some partition key parts are missing' when trying to insert or update.
Please consider that my application have to a lot of (near 300,000) writes in to database in a short time of interval , so i want to consider the maximum writes available in db.
May be it is a criteria can solve the issue ,'first read from db then write into db and use dummy values for primary key if it is not available at the moment of inserting or updating' . But it will take place more activities about a another copy of 300,000 reads in the db and it will slow the entire processes of db and my application.
So i looking for another solution.
four primary keys in that table and these primary keys not available at all the moment going to insert or update.
As you are finding out, that is not possible. For partition keys in particular, they are used (hashed) to determine which node in the cluster is primarily responsible for the data. As it is a fundamental part of the Cassandra write path, it must be complete at write-time and cannot be changed/updated later.
The same is true with clustering keys (keys which determine the on-disk sort order of all data within a partition). Omitting one or more will yield this message:
Some clustering keys are missing
Unfortunately, there isn't a good way around this. A row's keys must all be known before writing. It's worth mentioning that keys in Cassandra are unique, so any attempt to update them will result in a new row.
Perhaps writing the data to a streaming topic or message broker (like Pulsar or Kafka) beforehand would be a better option? Then, once all keys are known, the data (message) can consumed from the topic and written to Cassandra.
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).
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
I have this structure that I want a user to see the other user's feeds.
One way of doing it is to fan out an action to all interested parties's feed.
That would result in a query like select from feeds where userid=
otherwise i could avoid writing so much data and since i am already doing a read I could do:
select from feeds where userid IN (list of friends).
is the second one slower? I don't have the application yet to test this with a lot of data/clustering. As the application is big writing code to test a single node is not worth it so I ask for your knowledge.
If your title is correct, and userid is a secondary index, then running a SELECT/WHERE/IN is not even possible. The WHERE/IN clause only works with primary key values. When you use it on a column with a secondary index, you will see something like this:
Bad Request: IN predicates on non-primary-key columns (columnName) is not yet supported
Also, the DataStax CQL3 documentation for SELECT has a section worth reading about using IN:
When not to use IN
The recommendations about when not to use an index apply to using IN
in the WHERE clause. Under most conditions, using IN in the WHERE
clause is not recommended. Using IN can degrade performance because
usually many nodes must be queried. For example, in a single, local
data center cluster with 30 nodes, a replication factor of 3, and a
consistency level of LOCAL_QUORUM, a single key query goes out to two
nodes, but if the query uses the IN condition, the number of nodes
being queried are most likely even higher, up to 20 nodes depending on
where the keys fall in the token range.
As for your first query, it's hard to speculate about performance without knowing about the cardinality of userid in the feeds table. If userid is unique or has a very high number of possible values, then that query will not perform well. On the other hand, if each userid can have several "feeds," then it might do ok.
Remember, Cassandra data modeling is about building your data structures for the expected queries. Sometimes, if you have 3 different queries for the same data, the best plan may be to store that same, redundant data in 3 different tables. And that's ok to do.
I would tackle this problem by writing a table geared toward that specific query. Based on what you have mentioned, I would build it like this:
CREATE TABLE feedsByUserId
userid UUID,
feedid UUID,
action text,
PRIMARY KEY (userid, feedid));
With a composite primary key made up of userid as the partitioning key you will then be able to run your SELECT/WHERE/IN query mentioned above, and achieve the expected results. Of course, I am assuming that the addition of feedid will make the entire key unique. if that is not the case, then you may need to add an additional field to the PRIMARY KEY. My example is also assuming that userid and feedid are version-4 UUIDs. If that is not the case, adjust their types accordingly.