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Teradata is shared-nothing architecture. Of course it will outperform Databricks and Snowflake shared-disk model as the data is colocated on the compute nodes s

Teradata is shared-nothing architecture. Of course it will outperform Databricks and Snowflake shared-disk model as the data is colocated on the compute nodes so it doesn't have to travel anywhere. Good luck on your budget trying to scale up shared-nothing database and making it scale up and down based on workload without downtime. You can achieve significant speedup resembling shared-nothing databases by pushing the data close to the query using caching. Snowflake does it out of the box as it maintains table metadata. Databricks can do it too, but you have to be careful and it sucks.

mrbungie 6y ago
Fair enough, I'm just talking from the perspective of a SQL analyst who could use both Teradata and Databricks at work. Teradata was faster with no user-facing tuning vs tuned Databricks. And if you can pay for Teradata you may as well use it.
anonymousDan 6y ago
Wait what? I thought spark is designed to work on top of a shared nothing cluster? Have you any resources describing the architecture of databricks specifically?
- snidane 6y ago
  In Databricks you only get cloud storage (s3) as a persistent storage for data. So the data has to move from s3 to compute nodes. Self hosted HDFS will work more like shared nothing.
  - anonymousDan 6y ago
    Ah,thanks, makes sense.
ConnorLeet 6y ago
Do you have any resources or examples of challenges with scaling up a shared-nothing architecture? At the crossroads between a shared-nothing and shared disk decision at the moment.
- snidane 6y ago
  With shared-disk the data sits in cold storage (eg. s3) for which you pay cheaply. Only when you have to do some big computation you spin up a temporary compute cluster, do the computation, store resukt back in s3 and kill the cluster. You can also have long lived compute clusters and shut them or scale them down at night when nobody uses them. Since the data doesn't "live" in the compute nodes, you can add or remove as many compute nodes as you wish. In shared-nothing the data "lives" on the compute nodes, so you can't willy nilly add or remove nodes. The data would either get lost if you removed more nodes than what is necessary for triple replicated redundancy. Or if you added nodes you'd have to wait before the data gets rebalanced to those new nodes, resulting in massive reshuffling of everything. Keep in mind that shared-nothing clusters are most likely long lived and have multiple large datasets sitting on them, so by adding nodes you will start to shuffle everything around. With shared-disk your compute cluster is only a single use for one dataset and you don't lose data as you scale cluster up and down. There is a hybrid architecture which will give you both, but doesn't exist out there: Shared-disk with active caching. Ie. giving you control over which tables will be pinned down on your temporary compute-cluster. That will give you performance of shared-nothing but with convenience of shared-disk temporary compute clusters.
georgewfraser 6y ago
If Teradata is faster for workload X or Y, it isn’t because of a shared nothing architecture. Databricks and Snowflake both cache the working set on local disks of the worker nodes, so in practice they are shared nothing systems from a performance perspective.
- snidane 6y ago
  Only after using the data beforehand it gets cached or by actively pushing it into cache. Otherwise everything sits in cold storage far away. And having control over the cache sucks as hell. You can't pin down a table to reside in compute node disk cache when you know it will be used often.

mrbungie 6y ago

Fair enough, I'm just talking from the perspective of a SQL analyst who could use both Teradata and Databricks at work. Teradata was faster with no user-facing tuning vs tuned Databricks. And if you can pay for Teradata you may as well use it.

anonymousDan 6y ago

Wait what? I thought spark is designed to work on top of a shared nothing cluster? Have you any resources describing the architecture of databricks specifically?

snidane 6y ago
In Databricks you only get cloud storage (s3) as a persistent storage for data. So the data has to move from s3 to compute nodes. Self hosted HDFS will work more like shared nothing.
- anonymousDan 6y ago
  Ah,thanks, makes sense.

ConnorLeet 6y ago

Do you have any resources or examples of challenges with scaling up a shared-nothing architecture? At the crossroads between a shared-nothing and shared disk decision at the moment.

snidane 6y ago
With shared-disk the data sits in cold storage (eg. s3) for which you pay cheaply. Only when you have to do some big computation you spin up a temporary compute cluster, do the computation, store resukt back in s3 and kill the cluster. You can also have long lived compute clusters and shut them or scale them down at night when nobody uses them. Since the data doesn't "live" in the compute nodes, you can add or remove as many compute nodes as you wish. In shared-nothing the data "lives" on the compute nodes, so you can't willy nilly add or remove nodes. The data would either get lost if you removed more nodes than what is necessary for triple replicated redundancy. Or if you added nodes you'd have to wait before the data gets rebalanced to those new nodes, resulting in massive reshuffling of everything. Keep in mind that shared-nothing clusters are most likely long lived and have multiple large datasets sitting on them, so by adding nodes you will start to shuffle everything around. With shared-disk your compute cluster is only a single use for one dataset and you don't lose data as you scale cluster up and down. There is a hybrid architecture which will give you both, but doesn't exist out there: Shared-disk with active caching. Ie. giving you control over which tables will be pinned down on your temporary compute-cluster. That will give you performance of shared-nothing but with convenience of shared-disk temporary compute clusters.

georgewfraser 6y ago

If Teradata is faster for workload X or Y, it isn’t because of a shared nothing architecture. Databricks and Snowflake both cache the working set on local disks of the worker nodes, so in practice they are shared nothing systems from a performance perspective.

snidane 6y ago
Only after using the data beforehand it gets cached or by actively pushing it into cache. Otherwise everything sits in cold storage far away. And having control over the cache sucks as hell. You can't pin down a table to reside in compute node disk cache when you know it will be used often.