how

BigQuery broke my heart

Gabe Hamilton

Reporting Solutions Smackdown

We are evaluating replacements for SQL Server for our Reporting & Business Intelligence backend.

Many TBs of data.

Closer to SQL the less report migration we need to do.

We like saving money.

Solutions we've been testingRedshift

BigQuery

CouchDB

MongoDB

Cassandra

TerraData

Oracle

Plus various changes to our design

Some of these are necessary for certain technologies.

Denormalization

Sharding strategies

Nested data

Tune our existing Star Schema and Tables

BigQuery is

A massively parallel datastore

Columnar

Queries are SQL Select statements

Uses a Tree structure to distribute across nodes

How many nodes?

10,000 nodes!

And what price?

3.5 cents /GBResourcePricing

Query cost is per GB in the columns processed

Interactive Queries $0.035

Batch Queries $0.02

Storage $0.12 (per GB/month)

Which is great for our big queries

A gnarly query that looks at 200GB of data costs $7.50 in BigQuery.

If that takes 2 hours to run on a $60/hr cluster of a competing technology...

It's a little more complicated because in theory several of those queries could run simultaneously on the competing tech.

Still, that's 4 X cheaper plus the speed improvement.

Example: Github data from past year3.5 GB Table

SELECT type, count(*) as num FROM [publicdata:samples.github_timeline] group by type order by num desc;

Query complete (1.1s elapsed, 75.0 MB processed)Event Type numPushEvent 2,686,723CreateEvent 964,830WatchEvent 581,029IssueCommentEvent 507,724GistEvent 366,643IssuesEvent 305,479ForkEvent 180,712PullRequestEvent 173,204FollowEvent 156,427GollumEvent 104,808

Cost $0.0026

or 5 for a penny

It was love at first type.

But Then...

Reality

Uploaded our test dataset

Which is 250GB

Docs are good, tools are good.

Hurdle 1: only one join per query.

Ok, rewrite as ugly nested selects...

Result

Round 2

No problem, I had seen that joins were somewhat experimental.

Try the denormalized version of the data.

SELECT ProductId, StoreId, ProductSizeId, InventoryDate, avg(InventoryQuantity) as InventoryQuantity FROM BigDataTest.denorm GROUP EACH BY ProductId, StoreId, ProductSizeId, InventoryDate

1st error message helpfully says, try GROUP EACH BY

Final Result

It's not you, it's me The documentation had some semi-useful information:

Because the system is interactive, queries that produce a large number of groups might fail. The use of the TOP function instead of GROUP BY might solve the problem.

However, the BigQuery TOP function only operates on one column.

At this point I had jumped through enough hoops. I posted on Stack Overflow, the official support channel according to the docs, and have gotten no response.

Epilogue

Simplifying my query down to two grouping columns did cause it to run with a limit statement.

SELECT ProductId, StoreId, avg(InventoryQuantity) as InventoryQuantity FROM BigDataTest.denorm GROUP each BY ProductId, StoreId Limit 1000Query complete (4.5s elapsed, 28.1 GB processed)

Without a limit it gives Error: Response too large to return.Perhaps there is still hope for me and BigQuery...

Me

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@gabehamilton

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