ClickHouse 现在提供了一种原生的 JSON 列类型,旨在处理半结构化和动态数据。重要的是要澄清,这是一种列类型,而不是一种数据格式——您可以将 JSON 作为字符串插入到 ClickHouse 中,或通过支持的格式,如 JSONEachRow,但这并不意味着使用 JSON 列类型。用户应仅在数据结构动态时使用 JSON 类型,而不是仅仅因为他们存储 JSON。
何时使用 JSON 类型
当您的数据:
- 具有 不可预测的键,可能会随着时间而变化。
- 包含 具有不同类型的值(例如,路径有时可能包含字符串,有时可能是数字)。
- 需要模式灵活性,严格类型不可行的情况下。
如果您的数据结构已知且一致,通常没有必要使用 JSON 类型,即使您的数据是 JSON 格式。具体而言,如果您的数据具有:
- 已知键的扁平结构:使用标准列类型,例如 String。
- 可预测的嵌套:使用 Tuple、Array 或 Nested 类型来处理这些结构。
- 具有不同类型的可预测结构:可以考虑使用 Dynamic 或 Variant 类型。
您还可以混合使用方法 - 例如,使用静态列来处理可预测的顶级字段,并使用单个 JSON 列来处理有效负载的动态部分。
使用 JSON 的注意事项和提示
JSON 类型通过将路径扁平化为子列来实现高效的列式存储。但灵活性也带来了责任。要有效使用它:
类型提示
类型提示不仅仅是避免不必要的类型推断,还完全消除存储和处理间接性。具有类型提示的 JSON 路径总是像传统列一样存储,避免了在查询时需要 区分符列 或动态解析。这意味着,使用良好定义的类型提示,嵌套的 JSON 字段可以实现与它们从一开始就作为顶级字段建模时相同的性能和效率。因此,对于大部分一致但仍然受益于 JSON 灵活性的数据集,类型提示提供了一种便捷的方式来保持性能,而不需要重构您的模式或摄取管道。
高级功能
有关更多指导,请参见 ClickHouse JSON 文档 或浏览我们的博客文章 ClickHouse 的新强大 JSON 数据类型。
考虑以下 JSON 样本,表示来自 Python PyPI 数据集 的一行:
{
"date": "2022-11-15",
"country_code": "ES",
"project": "clickhouse-connect",
"type": "bdist_wheel",
"installer": "pip",
"python_minor": "3.9",
"system": "Linux",
"version": "0.3.0"
}
假设这个结构是静态的,并且可以明确定义类型。即使数据是 NDJSON 格式(每行一个 JSON),对于这样的结构也不需要使用 JSON 类型。只需使用经典类型定义模式即可。
CREATE TABLE pypi (
`date` Date,
`country_code` String,
`project` String,
`type` String,
`installer` String,
`python_minor` String,
`system` String,
`version` String
)
ENGINE = MergeTree
ORDER BY (project, date)
并插入 JSON 行:
INSERT INTO pypi FORMAT JSONEachRow
{"date":"2022-11-15","country_code":"ES","project":"clickhouse-connect","type":"bdist_wheel","installer":"pip","python_minor":"3.9","system":"Linux","version":"0.3.0"}
考虑 arXiv 数据集,其中包含 250 万篇学术论文。该数据集中每一行,按 NDJSON 格式分发,代表一篇已发表的学术论文。以下是一个示例行:
{
"id": "2101.11408",
"submitter": "Daniel Lemire",
"authors": "Daniel Lemire",
"title": "Number Parsing at a Gigabyte per Second",
"comments": "Software at https://github.com/fastfloat/fast_float and\n https://github.com/lemire/simple_fastfloat_benchmark/",
"journal-ref": "Software: Practice and Experience 51 (8), 2021",
"doi": "10.1002/spe.2984",
"report-no": null,
"categories": "cs.DS cs.MS",
"license": "http://creativecommons.org/licenses/by/4.0/",
"abstract": "With disks and networks providing gigabytes per second ....\n",
"versions": [
{
"created": "Mon, 11 Jan 2021 20:31:27 GMT",
"version": "v1"
},
{
"created": "Sat, 30 Jan 2021 23:57:29 GMT",
"version": "v2"
}
],
"update_date": "2022-11-07",
"authors_parsed": [
[
"Lemire",
"Daniel",
""
]
]
}
尽管这里的 JSON 是复杂的,具有嵌套结构,但它是可预测的。字段的数量和类型不会改变。虽然我们可以在这个示例中使用 JSON 类型,但我们也可以简单地使用 Tuples 和 Nested 类型明确地定义结构:
CREATE TABLE arxiv
(
`id` String,
`submitter` String,
`authors` String,
`title` String,
`comments` String,
`journal-ref` String,
`doi` String,
`report-no` String,
`categories` String,
`license` String,
`abstract` String,
`versions` Array(Tuple(created String, version String)),
`update_date` Date,
`authors_parsed` Array(Array(String))
)
ENGINE = MergeTree
ORDER BY update_date
我们还可以将数据作为 JSON 插入:
INSERT INTO arxiv FORMAT JSONEachRow
{"id":"2101.11408","submitter":"Daniel Lemire","authors":"Daniel Lemire","title":"Number Parsing at a Gigabyte per Second","comments":"Software at https://github.com/fastfloat/fast_float and\n https://github.com/lemire/simple_fastfloat_benchmark/","journal-ref":"Software: Practice and Experience 51 (8), 2021","doi":"10.1002/spe.2984","report-no":null,"categories":"cs.DS cs.MS","license":"http://creativecommons.org/licenses/by/4.0/","abstract":"With disks and networks providing gigabytes per second ....\n","versions":[{"created":"Mon, 11 Jan 2021 20:31:27 GMT","version":"v1"},{"created":"Sat, 30 Jan 2021 23:57:29 GMT","version":"v2"}],"update_date":"2022-11-07","authors_parsed":[["Lemire","Daniel",""]]}
假设添加了另一个名为 tags
的列。如果这只是一个字符串列表,我们可以建模为 Array(String)
,但假设用户可以添加任意标签结构,混合类型(注意 score 是字符串或整数)。我们的修改 JSON 文档:
{
"id": "2101.11408",
"submitter": "Daniel Lemire",
"authors": "Daniel Lemire",
"title": "Number Parsing at a Gigabyte per Second",
"comments": "Software at https://github.com/fastfloat/fast_float and\n https://github.com/lemire/simple_fastfloat_benchmark/",
"journal-ref": "Software: Practice and Experience 51 (8), 2021",
"doi": "10.1002/spe.2984",
"report-no": null,
"categories": "cs.DS cs.MS",
"license": "http://creativecommons.org/licenses/by/4.0/",
"abstract": "With disks and networks providing gigabytes per second ....\n",
"versions": [
{
"created": "Mon, 11 Jan 2021 20:31:27 GMT",
"version": "v1"
},
{
"created": "Sat, 30 Jan 2021 23:57:29 GMT",
"version": "v2"
}
],
"update_date": "2022-11-07",
"authors_parsed": [
[
"Lemire",
"Daniel",
""
]
],
"tags": {
"tag_1": {
"name": "ClickHouse user",
"score": "A+",
"comment": "A good read, applicable to ClickHouse"
},
"28_03_2025": {
"name": "professor X",
"score": 10,
"comment": "Didn't learn much",
"updates": [
{
"name": "professor X",
"comment": "Wolverine found more interesting"
}
]
}
}
}
在这种情况下,我们可以将 arXiv 文档建模为全部 JSON 或者仅添加一个 JSON tags
列。我们在下面提供两种示例:
CREATE TABLE arxiv
(
`doc` JSON(update_date Date)
)
ENGINE = MergeTree
ORDER BY doc.update_date
备注
我们在 JSON 定义中为 update_date
列提供了一个类型提示,因为我们在排序/主键中使用它。这有助于 ClickHouse 知道该列不会为空,并确保它知道要使用哪个 update_date
子列(对于每种类型可能会有多个,因此否则会产生歧义)。
我们可以将数据插入此表,并使用 JSONAllPathsWithTypes
函数和 PrettyJSONEachRow
输出格式查看随后的推断模式:
INSERT INTO arxiv FORMAT JSONAsObject
{"id":"2101.11408","submitter":"Daniel Lemire","authors":"Daniel Lemire","title":"Number Parsing at a Gigabyte per Second","comments":"Software at https://github.com/fastfloat/fast_float and\n https://github.com/lemire/simple_fastfloat_benchmark/","journal-ref":"Software: Practice and Experience 51 (8), 2021","doi":"10.1002/spe.2984","report-no":null,"categories":"cs.DS cs.MS","license":"http://creativecommons.org/licenses/by/4.0/","abstract":"With disks and networks providing gigabytes per second ....\n","versions":[{"created":"Mon, 11 Jan 2021 20:31:27 GMT","version":"v1"},{"created":"Sat, 30 Jan 2021 23:57:29 GMT","version":"v2"}],"update_date":"2022-11-07","authors_parsed":[["Lemire","Daniel",""]],"tags":{"tag_1":{"name":"ClickHouse user","score":"A+","comment":"A good read, applicable to ClickHouse"},"28_03_2025":{"name":"professor X","score":10,"comment":"Didn't learn much","updates":[{"name":"professor X","comment":"Wolverine found more interesting"}]}}}
SELECT JSONAllPathsWithTypes(doc)
FROM arxiv
FORMAT PrettyJSONEachRow
{
"JSONAllPathsWithTypes(doc)": {
"abstract": "String",
"authors": "String",
"authors_parsed": "Array(Array(Nullable(String)))",
"categories": "String",
"comments": "String",
"doi": "String",
"id": "String",
"journal-ref": "String",
"license": "String",
"submitter": "String",
"tags.28_03_2025.comment": "String",
"tags.28_03_2025.name": "String",
"tags.28_03_2025.score": "Int64",
"tags.28_03_2025.updates": "Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))",
"tags.tag_1.comment": "String",
"tags.tag_1.name": "String",
"tags.tag_1.score": "String",
"title": "String",
"update_date": "Date",
"versions": "Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))"
}
}
1 row in set. Elapsed: 0.003 sec.
或者,我们可以使用之前的模式和一个 JSON tags
列来建模。这通常是更优选的,最小化 ClickHouse 所需的推断:
CREATE TABLE arxiv
(
`id` String,
`submitter` String,
`authors` String,
`title` String,
`comments` String,
`journal-ref` String,
`doi` String,
`report-no` String,
`categories` String,
`license` String,
`abstract` String,
`versions` Array(Tuple(created String, version String)),
`update_date` Date,
`authors_parsed` Array(Array(String)),
`tags` JSON()
)
ENGINE = MergeTree
ORDER BY update_date
INSERT INTO arxiv FORMAT JSONEachRow
{"id":"2101.11408","submitter":"Daniel Lemire","authors":"Daniel Lemire","title":"Number Parsing at a Gigabyte per Second","comments":"Software at https://github.com/fastfloat/fast_float and\n https://github.com/lemire/simple_fastfloat_benchmark/","journal-ref":"Software: Practice and Experience 51 (8), 2021","doi":"10.1002/spe.2984","report-no":null,"categories":"cs.DS cs.MS","license":"http://creativecommons.org/licenses/by/4.0/","abstract":"With disks and networks providing gigabytes per second ....\n","versions":[{"created":"Mon, 11 Jan 2021 20:31:27 GMT","version":"v1"},{"created":"Sat, 30 Jan 2021 23:57:29 GMT","version":"v2"}],"update_date":"2022-11-07","authors_parsed":[["Lemire","Daniel",""]],"tags":{"tag_1":{"name":"ClickHouse user","score":"A+","comment":"A good read, applicable to ClickHouse"},"28_03_2025":{"name":"professor X","score":10,"comment":"Didn't learn much","updates":[{"name":"professor X","comment":"Wolverine found more interesting"}]}}}
现在我们可以推断子列 tags 的类型。
SELECT JSONAllPathsWithTypes(tags)
FROM arxiv
FORMAT PrettyJSONEachRow
{
"JSONAllPathsWithTypes(tags)": {
"28_03_2025.comment": "String",
"28_03_2025.name": "String",
"28_03_2025.score": "Int64",
"28_03_2025.updates": "Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))",
"tag_1.comment": "String",
"tag_1.name": "String",
"tag_1.score": "String"
}
}
1 row in set. Elapsed: 0.002 sec.