tensorbase / tensorbase Goto Github PK

如题

What is the status of this project? I only see an initial commit m0 and hardly any coding changes afterwards. Is this project terminated?

should be partially ok...

a quick fix is using a 3rd party lib as slow path.

basically we have, just need some meta works + some tests is enough:)

Utilizing the latest version of the Clickhouse-Native-JDBC driver available from Maven (2.5.4), I attempted to connect to TB, but an unsupported client version exception is thrown.

@jinmingjian Jin, please let me know if you would also like me to try testing programmatically via Java code directly.

• test with absolute data path, which causes test failed for others.
  we should use relative data path or use /tmp directory which exists any linux system.
• test with data which isn't exists at this repository.

Hello,

I am very interested in your project and I am attempting to begin testing it out. However, the documentation for tools that exist in m0 does not seem to be accurate (baseops, baseshell). Subsequently, attempting to use the clickhouse client to create a very simple table using ddl fails. I am not sure what to use for ENGINE although it appears to be required and using MergeTree fails. I tried with and without ORDER BY. Any assistance you can provide would be greatly appreciated.

-Chris Whelan

TensorBase :) create table sales (title string) ENGINE = MergeTree ORDER BY title;

CREATE TABLE sales
(
    `title` string
)
ENGINE = MergeTree
ORDER BY title

Query id: 22fd667c-851d-4087-9fb7-5a58128003de


0 rows in set. Elapsed: 0.001 sec.

Received exception from server (version 2021.3.0):
Code: 3. DB::Exception: Received from localhost:9528. WrappingLangError(ASTError). Error when AST processing.

• ClickHouse HTTP protocol support
• MySQL

Building tensorbase may cost 5 ~ 6 minutes for linking.

    Building [=======================> ] 332/333: server(bin)

• github action for building
• github action for testing
• github action for coverage
• add github performance bot
• Dependabot

via data-gen

part of #23

Ref: https://github.com/rust-lang/mdBook

Hi, the documentation indicates that the string data type is supported, but attempting to insert a string into an existing tables fails with NoFixedSizeDataTypeError.

SHOW CREATE TABLE sales

Query id: e96c5bbe-52ad-4fcc-9df0-1afdab76700e

┌─statement─────────────────────────────────────────────────┐
│ create table sales ( Region String ) ENGINE = BaseStorage │
└───────────────────────────────────────────────────────────┘

1 rows in set. Elapsed: 0.000 sec.

TensorBase :) insert into sales (Region) values ('North')

INSERT INTO sales (Region) VALUES

Query id: 98e40494-bbbc-461d-bb7d-7e7798987b4d


1 rows in set. Elapsed: 0.001 sec.

Received exception from server (version 2021.3.0):
Code: 4. DB::Exception: Received from localhost:9528. WrappingMetaError(NoFixedSizeDataTypeError). No fixed size for dynamic sized data type.

The same problem occurs with Decimal(x,y) data types.

It's not an easy way to try tensorbase.

The Blog Hello, Base has some docs about nyc_taxi datasets benchmarks with ClickHouse. Yet the dataset is pretty large, it's hard for users to explorer tensorbase quickly.

Maybe we can implement some table functions like numbers or number_mt in ClickHouse.

• primary keys and row deduplication

part of #18

...

partition pruning based on the condition of partition key

@jinmingjian , Jin, I attempted to connect to the TB server using the Clickhouse JDBC driver (pulled in by DBeaver) on port 9528, but the connection attempt times out on the read. I tried configuring the connection properties both with no Database/Schema specified as well as with default. I also tried with the No authentication option set. Below are screen captures illustrating the connection properties and the connection error. Note that I also confirmed that the firewall is turned off.

When click the Slack Channel link in README or in official website, it redirects you to tensorbase's official Slack Channel link: https://tensorbase.slack.com/, but without an invitation. So I think newcomers cannot log in.

I can log in other slack workspaces like Kubernetes, so I guess it's just because tensorbase's slack link is not an invitation link. See that of k8s', there is a button says "GET MY INVITE" for people who are not in the group.

Why

Arrow-DataFusion has already supported the parts of TPC-H. But TensorBase does not support the storage of all that data types. To enable this benchmarks, it makes TensorBase more feature-mature.

How

From the Arrow-DataFusion, we should support the following type: DataType::Float64, DataType::Utf8, DataType::Date32. However, this is not economical and performance way. Firstly, it suggest enable Decimal, String, Datetime.

TODO

• support Datetime type
• support Date type #54
• support String type #22
• support Decimal type #26

it may adapt to other existed clients to some extent when necessary:
clickhouse, rdbc, odbc, jdbc, mysql, pg

• basic data layout and data partition design
• enable data write path
• add server client arch
  • add server
  • add client driver(rust)
  • change baseops to use client driver
  • change baseshell to use client driver
• basic stress tests and benchmarks

the logic in arrow/DF ignores timezone, but CH re-interpret the presentation according to the server's timezone.

WHY

Currently, TensorBase only supports single node mode. A single node may not have enough space for all the data and we need to store them in a distributed manner. By introducing components like Ballista, we can enable TB to support distributed storage and query.

HOW

Currently, a ClickHouse compatible SQL query will be parsed and passed to TB/engine, TB/engine will then invoke DataFusion to execute the query. To support distributed storage and query, we can add a distributed engine (e.g., Ballista) between TB/engine and DataFusion.

For example, when TB is configured to use Ballista to support distributed storage and query, TB/engine can act as a Ballista client and send ExecuteQuery to Ballista scheduler. The scheduler will then distribute the work to executer(s). For more details about the architecture of Ballista, please refer to this doc.

In the future, TB may support different distributed engines other than Ballista. We should be able to integrate them in a similar manner.

TODO

• Add Ballista as a distributed storage and query engine
  • Add arrow-datafusion/ballista to TB #87

if uses DataFusion Ballista, this feature may be easily achieved. not sure who wants to try firstly:)

another fixed length type

TB now already survives from the application crash. it is nice to have a WAL to protect against the kernel crash or machine sudden shutdown.

Is it possible to consider joining a foundation to ensure opensource continuity. Since this is AL 2.0 perhaps ASF may be a good fit if you can be accepted, otherwise there are other foundation like the Linux Foundation, Cloud Native Foundation, etc. which you can approach.

• LowCardinality String
• String/Blob
• Decimal
• several other fixed length types (low hanging fruits)
• Nullable types

The easiest way to support builtin functions seems to be by using ScalarUDF or AggregateUDF.

Changes

To support this the following changes would have to be made

lightjit::builtins

We have to convert the functions into type datafusion::ScalarUDF.

Also, add a get_udf() function that matches a string to the function.

Should we still have these located here or maybe move them into a different crate?

lang::parse

add a new function parse_builtins(p). This would be similar to the current parse_tables(p) function but look for any builtins and return a HashSet of the ones found.

engine::run()

add let builtins = parse::parse_builtins(p)?;. We would have to also add this field to datafusion::run the same as tabs or cols.

    let (tabs, cols) = parse::parse_tables(p)?;
    let builtins = parse::parse_tables(p)?; // <--- New
    log::debug!("projections - tabs: {:?}, cols: {:?}", tabs, cols);
    datafusions::run(ms, ps, current_db, raw_query, query_id, tabs, cols, builtins, qs) // <- also pass builtins

engine::datafusion::run()

Before running we can check the sql if any builtins are used. If used all we need is to cxt.register_udf(builtin).

Sudo code

let mut ctx = ExecutionContext::new();
...
if !builtins.is_empty() {
    for f in builtins.drain() {
        let udf = get_udf(f)?;
        ctx.register_udf();
    }
}
let df = ctx.sql(raw_query)?;
...

Decimal32: Decimal(9,2)
Decimal64: Decimal(9,2)

more are here: https://clickhouse.tech/docs/en/sql-reference/data-types/decimal/

"getting started" section should work on windows 10 wsl2. but I have no machine for the verification temporarily...

part of #18

generally, all support to fixed length type is easy to add. Just pick up one currently implemented type as an example:)