Materializing tables with Vitess

I wrote this post as I was playing with materializing tables in Vitess. I find that there’s not that many resources online that walk through Vitess features. I hope this post is useful for whoever is looking at Vitess capabilities.


Let’s imagine an example of the following schema in an abstract ecommerce app:

CREATE TABLE `products` (
  `id` bigint(20) NOT NULL AUTO_INCREMENT,
  `tenant_id` bigint(20) DEFAULT NULL,
  `title` varchar(255) DEFAULT NULL,
  `product_type` varchar(255) DEFAULT NULL,
  PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

In the rendering layer of the ecommerce app, you may find the following query appear as a hot one:

SELECT DISTINCT product_type
FROM products
WHERE tenant_id = %tenant_id%

The problem is, this query is O(N) complex and it might take significant I/O time to execute. Even if you cache it heavily, cache misses would be slow.

Let’s see how we can leverage materialize feature that comes with Vitess and build results of that table ahead of time.

Experiment

We’ll assume the commerce schema has our products table, and the storefront schema is where we want stuff to be materialized.

-- create materialized table on storefront keyspace
CREATE TABLE `product_types_by_tenant` (
  `tenant_id` bigint(20) DEFAULT NULL,
  `product_type` varchar(255) DEFAULT NULL,
  UNIQUE KEY `index_tenant_id_and_product_type` (`tenant_id`,`product_type`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

Creating a materialized view is as easy as:

$ vtctlclient Materialize '{"workflow": "product_types_by_tenant", "source_keyspace": "commerce", "target_keyspace": "storefront",
    "table_settings": [{"target_table": "product_types_by_tenant",
    "source_expression": "select tenant_id, product_type from products group by tenant_id, product_type"}],
    "cell": "zone1", "tablet_types": "REPLICA"}'

# to monitor
$ vtctlclient  Workflow storefront.product_types_by_tenant show

SELECT tenant_id, product_type FROM products GROUP BY tenant_id, product_type is our query that is passed as an argument to Materialize. It will get us the same result as SELECT DISTINCT product_type does for a single shop. Check materialize docs to see how the rest of arguments are built.

If you’re curious how the status of the stream looks like:

{
  "Workflow": "product_types_by_tenant",
  "SourceLocation": {
    "Keyspace": "commerce",
    "Shards": [
      "-"
    ]
  },
  "TargetLocation": {
    "Keyspace": "storefront",
    "Shards": [
      "-"
    ]
  },
  "MaxVReplicationLag": 1,
  "ShardStatuses": {
    "-/zone1-0428408676": {
      "MasterReplicationStatuses": [
        {
          "Shard": "-",
          "Tablet": "zone1-0428408676",
          "ID": 18,
          "Bls": {
            "keyspace": "commerce",
            "shard": "-",
            "filter": {
              "rules": [
                {
                  "match": "product_types_by_tenant",
                  "filter": "select tenant_id, product_type from products group by tenant_id, product_type"
                }
              ]
            }
          },
          "Pos": "MySQL56/53df5a9f-a5d0-11eb-a395-ce273039402d:1-282,549e34c6-a5d0-11eb-b33a-6a94ed0715c9:1-771072",
          "StopPos": "",
          "State": "Running",
          "DBName": "vt_storefront",
          "TransactionTimestamp": 1620643932,
          "TimeUpdated": 1620643933,
          "Message": "",
          "CopyState": null
        }
      ],
      "TabletControls": null,
      "MasterIsServing": true
    }
  }
}
# query from source
$ mysql commerce -e 'SELECT DISTINCT product_type FROM products WHERE tenant_id = 1'

# query from materialized
$ mysql storefront -e 'SELECT product_type FROM product_types_by_tenant WHERE tenant_id = 1'

Results of these two statements become identical – the only difference is that the latter is a lot more efficient.

Things to note

  • Schema changes. You can manage do perform a schema change that would break your materialize stream. I applied ALTER TABLE products ADD some_new_column VARCHAR(255) just to see if it breaks and the stream stayed healthy. That means it should work fine for new columns added, but I still think it would break if you do anything to materilized columns.

  • Limited queries. Unfortunately, the subset of SELECT you can do in the materialized query is limited. It only supports simple WHERE clauses and it doesn’t support JOINs. Browse replicator_plan_test.go to see what kind of queries are supported.

  • Performance. There are concerns with VReplication’s performance that might become a problem on large tables with heavy write throughput. Hopefully those will be addressed soon.

  • Failure modes. If something breaks and you restart VReplicationExec, you’re the one responsible to keep the table clean. There’s nothing there that would verify that a half-copied table is correct.

I’m excited to see technologies like Vitess making complex stuff like replicating and denormalizing a subset of data so much easier, and I’m looking forward for more investments to come into the Vitess ecosystem.

Written in May 2021.
Kir Shatrov

Kir Shatrov helps businesses to grow by scaling the infrastructure. He writes about software, scalability and the ecosystem. Follow him on Twitter to get the latest updates.