We're looking for a tool (preferably open source) which helps us to perform complex queries (advanced filtering and joins, no need full SQL) in real time.
Assume that all the data needed fits in memory, and we want to avoid, if possible, the overhead of map reduce tools.
To be more specific, we need to load n partitions of a single table, and join them by clustering column.
Variables Table:
Variable ID: Partition key
Person ID: Clustering key
Variable Value
Desired output columns:
Person ID, Variable 1 Value, Variable 2 Vale, ..., Variable N Value
We can achieve it by an in-memory load-filter-join process, but we were wondering if there's any tool out there with this use case covered out of the box and with a fair performance.
We've tested Spark, but the partitioning of Spark C* connector is based on the primary key, so each Variable ID would be loaded in a different Spark node, and the join process would be really slow (all the data would travel all over the Spark cluster).
Any tips? known tools?
I believe that you have a number of options to perform this task:
Rethink your database schema, denormalize it. var_id:person_id:value rows are not the best table schema if you want to query by person_id (and it smells really bad as an entity-attribute-value db antipattern):
EAV gives a flexibility to the developer to define the schema as needed and this is good in some circumstances. On the other hand it performs very poorly in the case of an ill-defined query and can support other bad practices. In other words, EAV gives you enough rope to hang yourself and in this industry, things should be designed to the lowest level of complexity because the guy replacing you on the project will likely be an idiot.
You can use schema with multiple columns (cassandra can handle a lot of them):
create table person_data (
person_id int primary key,
var1 text,
var2 text,
var3 text,
var4 text,
....
);
If you don't have a predefined set of variables, you can use cql3 collections like map for storing the data in a more flexible way.
Create a secondary index on person_id (even it's a clustering key already). You can query for all data for a specific user without using joins, but with some issues:
As your query will hit multiple partitions, it will require not a single disk seek, but a series of them, so your query latency may be higher than you're expecting.
secondary indexes are not free: C* must perform more work under the hood if you insert a row to a table with indexed columns.
Use external index like ElasticSearch/Solr if you plan to have a lot of complex queries which do not fit well into cql3.
Related
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.
Suppose that I have 1000 entities with exactly the same structure. For example all entities have three fields:
String id;
String name;
int amount;
Also I expect that there will be huge amount of every type of entity in the system.
So I have two variants right now:
For each entity create separate table which looks like:
CREATE TABLE <SOME_ENTITY_NAME> (
id text PRIMARY KEY,
name text,
amount int
)
I'll create only one table but with composite priamry key:
CREATE TABLE ALL_ENTITIES_TABLE (
entity_name text,
id text,
name text,
amount int,
PRIMARY KEY ((entity_name, id))
);
Of course, supporting only one table is more simplier, but what is with performance?
So, the question is what variant is better in terms of performance, taking into account that each type of entity will have millions(may be billions) of records?
There is a limitation on the number of the tables that can be created in the Cassandra cluster. Usual recommendation is too keep this number lower than 200, with ~500 is like a "hard stop"...
The reason for this is that every table requires allocation of additional memory, and other resources to keep auxiliary data, like, key/row caches, bloom filters, etc. Depending on the Cassandra version, every table may require 1-2Mb of memory.
So in your case, the 2nd design is better because you keep all data in single table, and your partition key will allow to spread data evenly between nodes of cluster.
In my opinion the first approach is incorrect in terms of maintainability. Too much of dynamically created tables should be tough to maintain. Also, If you use your partitioning/clustering order properly (as per the need of data retrieval) it should be easier and efficient to query. Also if you are using 3.x version of Cassandra, secondary indexes can come in handy.
NOTE: Secondary indexes don't allow sorting.
Cassandra was designed around the fact that disk space is the cheapest resource among all. You must build your data model around the queries that you will be using the most regardless of whether this model would consume more disk space or not - as long as it serves the purpose of your queries in the most efficient way. I wouldn't be able to answer your question without taking a look at the queries you will be using. In general, you must feel free to create as many tables as needed as long as it serves the purpose of your queries. I would recommend having a look here.
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.
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.