We are looking at Amazon Redshift to implement our Data Warehouse and I would like some suggestions on how to properly design Schemas in Redshift, please.
I am completely new to Redshift. In the past when I worked with "traditional" data warehouses, I was used to creating schemas such as "Source", "Stage", "Final", etc. to group all the database objects according to what stage the data was in.
By default, a database in Redshift has a single schema, which is named PUBLIC. So, my question to those who have worked with Redshift, does the approach that I have outlined above apply here? If not, I would love some suggestions.
Thanks.
With my experience in working with Redshift, I can assert the following points with confidence:
Multiple schema: You should create multiple schema and create tables accordingly. When you'll scale, it'll be easier for you to pin-point where exactly the table is supposed to be. Let us say, you have 3 schema, named production, aggregates and rough. Now, you know that the table production will contain the tables that are not supposed to be changed (mostly OLTP data) - such as user, order, transactions tables. Table aggregates will have aggregated data built over raw tables - such as number of orders placed per user per day per category. Finally, rough will contain any table that doesn't hold a business logic but is required for some temporary work - let us say to check the genre of movies for a list of 1 lakh users, which is shared with you in an excel file. Simply create a table in rough schema, perform your operations and drop the table. Now you very clearly know where you'll find the tables based on whether they are raw, aggregated or simply temporary tables.
Public schema: Forget it exists. Any table that is not preceded with a schema name, gets created there. A lot of clutter - no point in storing any important data there.
Cross schema joins: There's no stopping here. You may join as many tables from as many schema as required. In fact, it is desirable you create dimension tables and join on a PK later, rather than to keep all the information in a single table.
Spend some quality time in designing the schema and underlying table structure. When you expand, it'll be easier for you to classify things better in terms of access control. Do let me know if I've missed some obvious points.
You can have multiple databases in a Redshift cluster but I would stick with one. You are correct that schemas (essentially namespaces) are a good way to divide things up. You can query across schemas but not databases.
I would avoid using the public schema as managing certain permissions there can be difficult (easier to deny someone access to public than prevent them from being able to create a table for example).
For best results if you have the time, learn about the permissions system up front. You want to create groups that have access to schemas or tables and add/remove users from groups to control what they can do. Once you have that going it becomes pretty easy to manage.
In addition to the other responses, here are some suggestions for improving schema performance.
First: Automatic compression encodings using COPY command
Improve the performance of Amazon Redshift using the COPY command. It will get data into Redshift database. The COPY command is clever enough. It automatically chooses the most appropriate encoding settings for the data it uploads. You don’t have to think about it. However, it does so only for the first data upload into an empty table.
So, make sure to use a significant data set while uploading data for the first time, which Redshift can assess to set the column encodings in the best way. Uploading a few lines of test data will confuse Redshift to know how best to optimize the compression to handle the real workload.
Second: Use Best Distribution Style and Key
Distribution-style decides how data is distributed across the nodes. Applying a distribution style at table level tells Redshift how you want to distribute the table and the key. So, how you specify distribution style is important for good query performance with Redshift. The style you choose may affect requirements for data storage and cluster. It also affects the time taken by the COPY command to execute.
I recommend setting the distribution style to all tables with a smaller dimension. For large dimension, distribute both the dimension and associated fact on their join column. To optimize the second large dimension, take the storage-hit and distribute ALL. You can even design the dimension columns into the fact.
Third: Use the Best Sort Key
A Redshift database maintains data in a table with an arrangement of a sort-key-column if specified. Since it’s sorted in each partition; each cluster node upholds its partition in predefined order. (While designing your Redshift schema, also consider the impact on your budget. Redshift is priced by amount of stored data and by the number of nodes.)
Sort key optimizes Amazon Redshift performance significantly. You can do it in many ways. First, use data filtering. If where-clause filters on a sort-key-column, it skips the entire data blocks. It’s because Redshift saves data in blocks. Each block header records the minimum and maximum sort key value. Filter outside of that range, the entire block may get skipped.
Alternatively, when joining two tables, sorted on their joint keys, the data is read in matching order. Also, you can merge-join without separate sort-steps. Joining large dimension to a large fact table will be easy with this method because neither will fit into a hash table.
Related
From architectural perspective: Could you please help me understand why NoSQL DynamoDB is so hype.
DynamoDB supports some of the world’s largest scale applications by
providing consistent, single-digit millisecond response times at any
scale.
I'm trying to critic, in order to understand WHY part of the question.
We always have to specify partition Key and key attribute while retrieving to get millisecond of performance
If I design RDBMS:
where primary key or alternate key (INDEXED) always needs to be specified by in the query
I can use partition key to find out in which database my data is stored.
Never do JOINs
Isn't it same as NoSQL kind of architecture without any marketing buzz around it?
We're shifting to DynamoDB anyways but this is my innocent curiosity, there must be a strong reason which RDMBS can't do. Let's skip backup and maintenance advantages etc.
You are conflating two different things.
The definition of NoSQL
There isn't one, at least not one that can apply in all cases.
In most uses, NoSQL databases don't force your data into the fixed-schema "rows and columns" of a relational database. Although modern relational databases, such as Postgres, support data types such as JSONB that would have E. F. Codd spinning in his grave.
DynamoDB is a document database: it is optimized for retrieving and updating single documents based on a unique key, and it does not restrict the fields that those documents contain (other than requiring the ones used for a key).
Distributed Databases
A distributed database stores data on multiple nodes, and is able to perform parallel queries on those nodes and combine the results.
There are distributed SQL database: Redshift and BigQuery are optimized for queries against large datasets that may include joins, while MySQL (and no doubt others) which can run multiple engines and distribute the queries between them. It is possible for SQL databases to perform joins, including joins that cross nodes, but such joins generally perform poorly.
DynamoDB distributes items on shards based on their partition key. This makes it very fast for retrieving single items, because the query can be directed to a single shard. It is much less performant when scanning for items that reside on multiple shards.
As you note in your question, you can implement a sharded document DB on top of a relational database (either using native JSON columns or storing everything in a CLOB that is parsed for each access). But enough other people have done this (including DynamoDB) that it doesn't make sense (to me, at least) to re-implement.
I am exploring AWS Athena to query files in s3. We have a separate service that writes data into s3 in the following structure:
data
/log1
/log2
/log3
All the files have the same schema.
Following is the schema of the files:
id (a random string id)
timestamp
value
However, we need to be able to query data in a single folder - log1, log2 along with querying all the data together.
One option is to create separate tables for these. However, the sub folders log1, log2, etc. correspond to a device and these could be in numbers of 100s or thousands. These names would be dynamic and will be entered by the user for querying. Also, there are other query capabilities we need such as querying data between two timestamps, etc. Such queries will be fired at the /data folder level.
What would be a good way to structure the folders and the corresponding tables? I have read multiple questions that suggest partitioning, but for my use case, I don't really understand how to partition the data. I am extremely new to Athena and still learning. Any advice would be much appreciated.
Thank you in advance.
Partitioning will have an impact on how much data will be scanned with by every query and therefore improving the performance and lowering the cost - a good explanation can be found in AWS Partitiong Data:
You can partition your data by any key. A common practice is to partition the data based on time, often leading to a multi-level partitioning scheme. For example, a customer who has data coming in every hour might decide to partition by year, month, date, and hour. Another customer, who has data coming from many different sources but loaded one time per day, may partition by a data source identifier and date.
If you query a partitioned table and specify the partition in the WHERE clause, Athena scans the data only from that partition.
There are also some good recommendations regarding the partitions in Top 10 Performance Tuning Tips for AWS Athena:
When deciding the columns on which to partition, consider the following:
Columns that are used as filters are good candidates for partitioning.
Partitioning has a cost. As the number of partitions in your table increases, the higher the overhead of retrieving and processing the partition metadata, and the smaller your files. Partitioning too finely can wipe out the initial benefit.
If your data is heavily skewed to one partition value, and most queries use that value, then the overhead may wipe out the initial benefit.
Athena released lately a new feature called Partition Projections which might be helpful in your case:
In partition projection, partition values and locations are calculated from configuration rather than read from a repository like the AWS Glue Data Catalog. Because in-memory operations are often faster than remote operations, partition projection can reduce the runtime of queries against highly partitioned tables.
Especially the Dynamic ID Partitioning could be interesting in your case.
How to partition in the end depends on the queries and how they are designed:
Most queries include a time frame? Then you should consider date as a partition
Most queries filter for a specific device (or a small amount of ids)? Then it might be a better choice to use device id as partition or at least trying to bucketing these. Also depends on the amount of rows per device to not make it too granular.
You can also partition by date and device id.
Since you already have a partition by device I would go for this in the beginning and use projections to query this data.
I am trying to automatically generate a data documentation in the Redshift cluster for all the maintained data products, but I am having trouble to do so.
Is there a way to fetch/store metadata about tables/columns in redshift directly?
Is there also some automatic way to determine what are the unique keys in a Redshift table?
For example an ideal solution would be to have:
Table location (cluster, schema, etc.)
Table description (what is the table for)
Each column's description (what is each column for, data type, is it a key column, if so what type, etc.)
Column's distribution (min, max, median, mode, etc.)
Columns which together form a unique entry in the table
I fully understand that getting the descriptions automatically is pretty much impossible, but I couldn't find a way to store the descriptions in redshift directly, instead I'd have to use 3rd party solutions or generally a documentation outside of the SQL scripts, which I'm not a big fan of, due to the way the data products are built right now. Thus having a way to store each table's/column's description in redshift would be greatly appreciated.
Amazon Redshift has the ability to store a COMMENT on:
TABLE
COLUMN
CONSTRAINT
DATABASE
VIEW
You can use these comments to store descriptions. It might need a bit of table joining to access.
See: COMMENT - Amazon Redshift
I’m quite new to NoSQL and DynamoDB and I used to RDBMS. I’m designing database for a game and we're using DynamoDB and AWS Lambda for our backend. I created a table name “Users” for player profile that contains the user information and resources. Because the game has inventory system I also created a table name “UserItems”.
It’s all good until I realized DynamoDB don’t have transaction and any operation that is executed on both table (for example using an item that increase resource) has a chance of failure on one table while success on other and will cause missing data which affect our customers.
So I was thinking maybe my multiple tables design is not good since it’s a habit of me to design multiple table when I’m working with RDBMS. Which let me to think of storing the entire “UserItems” as hash in “Users” but I’m not sure this is a good practice because the size of a single row in Users table will be really big (we may have 500 unique items per users) and each time I pull or put data from/to “Users” (most of the time don’t need “UserItems” data) the read/write throughput will be also really large.
What should I do, keep the multiple tables design and handle transaction manually or switch to single table design? Or maybe there is a 3rd option?
Updated: more information about my use case
Currently I have 2 tables
Users: UserId (key), Username, Gold
UserItems: UserId (partition key), ItemId (sort key), Name, GoldValue
Scenarios:
User buy an item: Users.Gold will be deduced, new UserItem will be add to UserItems table.
User sell an item: Users.Gold will be increased, the Item will be deleted from UserItems table.
In both scenarios above I will have to do 2 update operation for 2 tables which without transaction there is a chance one of them failed.
To solve that I consider using single table solution which is a single Users table with 4 columns UserId(key), Username, Gold, UserItems. However there are two things I'm worried about:
Data in UserItems might be come to big for a single cell because one user could have up to 500 items.
To add/delete item I have to pull the UserItems from dynamodb, add/delete item and then put it back into Users. So I have to do 1 read and 1 write operation for 1 action. And because of issue (1) the read/write data size could become really big.
FWIW, the AWS documentation on NoSQL Design for DynamoDB suggests to use a single table:
As a general rule, you should maintain as few tables as possible in a
DynamoDB application. As emphasized earlier, most well designed
applications require only one table, unless there is a specific reason
for using multiple tables.
Exceptions are cases where high-volume time series data are involved,
or datasets that have very different access patterns—but these are
exceptions. A single table with inverted indexes can usually enable
simple queries to create and retrieve the complex hierarchical data
structures required by your application.
NoSql database is best suited for non-trasactional data. If you bring normalization(splitting your data into multiple tables) into noSQL, then you are beating the whole purpose of it. If performance is what matters most, then you should consider only having a single table for your use case. DynamoDB supports Range Keys, and also supports Secondary Indices. For your usecase, it would be better to redesign your table to use Range Keys.
If you can share more details about your current table, maybe i can help you with more inputs.
I'm using AWS Redshift as a back-end to my tableau desktop. AWS cluster is running with two dc1.large nodes and database table which I'm analyzing is of 30GB (with redshift compression enabled), I chose Redshift over tableau extract for performance issue but seems like Redshift live connection is much slower than extract. Any suggestions where shall I look into?
To use Redshift as a backend for a BI platform like Tableau, there are four things you can do to address latency:
1) Concurrency: Redshift is not great at running multiple queries at the same time so before you start tuning the database, make sure your query is not waiting in line behind other queries. (If you are the only one on the cluster, this shouldn't be a problem.)
2) Table size: Whenever you can, use aggregate tables for better performance. Fewer rows to scan means less IO and faster turnaround!
3) Query complexity: Ideally, you want your BI tool to issue simple, fast performing queries. Make sure your source tables are fast, and that Tableau isn't being forced to do a bunch of joins. Also, if your query does need to join multiple tables, make sure any large tables have the same distribution key.
4) "Indexing": Technically, Redshift does not support true indexing, but you can get close to the same thing by using "interleaved" sort keys. Traditional compound sort keys won't help, but an interleaved sort key can allow you to quickly access rows from multiple vectors (date and customer_id, for instance) without having to scan the entire table.
Reality Check
After all of these things are optimized, you will often find that you still can't be as fast as a Tableau extract. Simply stated, a "fast" Tableau dashboard needs to return data to it's user in <5 seconds. If you have 7 visuals on your dashboard, and each of the underlying queries takes 800 milliseconds to return (which is super fast for a database query), then you still are just barely reaching your target performance. Now, if just one of those queries take 5 seconds or more, your dashboard is going to feel "slow" no matter what you do.
In Summary
Redshift can be tuned using the approach above, but it may or may not be worth the effort. The best applications for using a live Redshift query instead of Tableau Extracts are in cases where the data is physically too large to create an extract of, and when you require data at a level of granularity that makes pre-aggregation infeasible. One good strategy is to create your main dashboard using an extract so that exploration/discovery is as fast as possible, and then use direct (live) Redshift queries for your drill-through reports (for instance, when you want to see exactly which customers roll up into your totals).
Few pointers as below
1) Use vacuum & Analyze once your ETL completes
2) Have you created the Table with proper Dist key and Sort Key
3) Aggregation if it's ok from the point of Data Granularity, requirement etc
1.Remove cursor, tableau access data from redshift leader node using cursor. Cursor works iteratively. Thus, impacting the performance.
2. Perform manual analyze on the table, after running heavy load operations. https://docs.aws.amazon.com/redshift/latest/dg/r_ANALYZE.html
3.Check the dist key distribution to avoid data skewness and improve performance.