ClickHouse · blinkov · Mar 18, 2020 · Feb 18, 2020 · Feb 18, 2020 · Feb 19, 2020
diff --git a/.gitignore b/.gitignore
@@ -16,6 +16,7 @@
 /docs/publish
 /docs/edit
 /docs/website
+/docs/venv/
 /docs/tools/venv/
 /docs/en/single.md
 /docs/ru/single.md

diff --git a/docs/en/data_types/array.md b/docs/en/data_types/array.md
@@ -1,4 +1,4 @@
-# Array(T) {#data_type-array}
+# Array(T) { #data_type-array}
 
 Array of `T`-type items.
 

diff --git a/docs/en/data_types/boolean.md b/docs/en/data_types/boolean.md
@@ -1,6 +1,6 @@
 # Boolean Values
 
-There isn't a separate type for boolean values. They use the UInt8 type, restricted to the values 0 or 1.
+There is no separate type for boolean values. Use UInt8 type, restricted to the values 0 or 1.
 
 
 [Original article](https://clickhouse.tech/docs/en/data_types/boolean/) <!--hide-->
diff --git a/docs/en/data_types/date.md b/docs/en/data_types/date.md
@@ -3,7 +3,7 @@
 A date. Stored in two bytes as the number of days since 1970-01-01 (unsigned). Allows storing values from just after the beginning of the Unix Epoch to the upper threshold defined by a constant at the compilation stage (currently, this is until the year 2106, but the final fully-supported year is 2105).
 The minimum value is output as 0000-00-00.
 
-The date is stored without the time zone.
+The date value is stored without the time zone.
 
 
 [Original article](https://clickhouse.tech/docs/en/data_types/date/) <!--hide-->
diff --git a/docs/en/data_types/datetime.md b/docs/en/data_types/datetime.md
@@ -1,4 +1,4 @@
-# DateTime {#data_type-datetime}
+# DateTime { #data_type-datetime}
 
 Allows to store an instant in time, that can be expressed as a calendar date and a time of a day.
 

diff --git a/docs/en/data_types/datetime64.md b/docs/en/data_types/datetime64.md
@@ -1,4 +1,4 @@
-# DateTime64 {#data_type-datetime64}
+# DateTime64 { #data_type-datetime64}
 
 Allows to store an instant in time, that can be expressed as a calendar date and a time of a day, with defined sub-second precision
 
@@ -9,7 +9,7 @@ Syntax:
 DateTime64(precision, [timezone])
 ```
 
-Internally, stores data as number of 'ticks' since epoch start (1970-01-01 00:00:00 UTC) as Int64. The tick resolution is determined by the precision parameter. Additionally, the `DateTime64` type can store time zone that is the same for the entire column, that affects how the values of the `DateTime64` type values are displayed in text format and how the values specified as strings are parsed ('2020-01-01 05:00:01.000'). The time zone is not stored in the rows of the table (or in resultset), but is stored in the column metadata. See details in [DateTime](datetime.md).
+Internally, stores data as a number of 'ticks' since epoch start (1970-01-01 00:00:00 UTC) as Int64. The tick resolution is determined by the precision parameter. Additionally, the `DateTime64` type can store time zone that is the same for the entire column, that affects how the values of the `DateTime64` type values are displayed in text format and how the values specified as strings are parsed ('2020-01-01 05:00:01.000'). The time zone is not stored in the rows of the table (or in resultset), but is stored in the column metadata. See details in [DateTime](datetime.md).
 
 ## Examples
 
@@ -36,7 +36,7 @@ SELECT * FROM dt
 └─────────────────────────┴──────────┘
 ```
 
-* When inserting datetime as an integer, it is treated as an appropriately scaled Unix Timestamp (UTC). `1546300800000` (with precision 3) represents `'2019-01-01 00:00:00'` UTC. However, as `timestamp` column has `Europe/Moscow` (UTC+3) timezone specified, when outputting as string the value will be shown as `'2019-01-01 03:00:00'`
+* When inserting datetime as an integer, it is treated as an appropriately scaled Unix Timestamp (UTC). `1546300800000` (with precision 3) represents `'2019-01-01 00:00:00'` UTC. However, as `timestamp` column has `Europe/Moscow` (UTC+3) timezone specified, when outputting as a string the value will be shown as `'2019-01-01 03:00:00'`
 * When inserting string value as datetime, it is treated as being in column timezone. `'2019-01-01 00:00:00'` will be treated as being in `Europe/Moscow` timezone and stored as `1546290000000`.
 
 **2.** Filtering on `DateTime64` values

diff --git a/docs/en/data_types/decimal.md b/docs/en/data_types/decimal.md
@@ -1,7 +1,7 @@
 
 # Decimal(P, S), Decimal32(S), Decimal64(S), Decimal128(S)
 
-Signed fixed point numbers that keep precision during add, subtract and multiply operations. For division least significant digits are discarded (not rounded).
+Signed fixed-point numbers that keep precision during add, subtract and multiply operations. For division least significant digits are discarded (not rounded).
 
 ## Parameters
 
@@ -23,9 +23,9 @@ For example, Decimal32(4) can contain numbers from -99999.9999 to 99999.9999 wit
 
 ## Internal representation
 
-Internally data is represented as normal signed integers with respective bit width. Real value ranges that can be stored in memory are a bit larger than specified above, which are checked only on convertion from string.
+Internally data is represented as normal signed integers with respective bit width. Real value ranges that can be stored in memory are a bit larger than specified above, which are checked only on conversion from a string.
 
-Because modern CPU's do not support 128 bit integers natively, operations on Decimal128 are emulated. Because of this Decimal128 works signigicantly slower than Decimal32/Decimal64.
+Because modern CPU's do not support 128-bit integers natively, operations on Decimal128 are emulated. Because of this Decimal128 works significantly slower than Decimal32/Decimal64.
 
 ## Operations and result type
 
@@ -41,15 +41,15 @@ Rules for scale:
 - multuply: S = S1 + S2.
 - divide: S = S1.
 
-For similar operations between Decimal and integers, the result is Decimal of the same size as argument.
+For similar operations between Decimal and integers, the result is Decimal of the same size as an argument.
 
-Operations between Decimal and Float32/Float64 are not defined. If you really need them, you can explicitly cast one of argument using toDecimal32, toDecimal64, toDecimal128 or toFloat32, toFloat64 builtins. Keep in mind that the result will lose precision and type conversion is computationally expensive operation. 
+Operations between Decimal and Float32/Float64 are not defined. If you need them, you can explicitly cast one of argument using toDecimal32, toDecimal64, toDecimal128 or toFloat32, toFloat64 builtins. Keep in mind that the result will lose precision and type conversion is a computationally expensive operation.
 
-Some functions on Decimal return result as Float64 (for example, var or stddev). Intermediate calculations might still be performed in Decimal, which might lead to different results between Float64 and Decimal inputs with same values.
+Some functions on Decimal return result as Float64 (for example, var or stddev). Intermediate calculations might still be performed in Decimal, which might lead to different results between Float64 and Decimal inputs with the same values.
 
 ## Overflow checks
 
-During calculations on Decimal, integer overflows might happen. Excessive digits in fraction are discarded (not rounded). Excessive digits in integer part will lead to exception.
+During calculations on Decimal, integer overflows might happen. Excessive digits in a fraction are discarded (not rounded). Excessive digits in integer part will lead to an exception.
 
 ```sql
 SELECT toDecimal32(2, 4) AS x, x / 3
@@ -86,7 +86,7 @@ SELECT toDecimal32(4.2, 8) AS x, 6 * x
 └────────────┴──────────────────────────────────┘
 ```
 
-Overflow checks happen not only on arithmetic operations, but also on value comparison:
+Overflow checks happen not only on arithmetic operations but also on value comparison:
 
 ```sql
 SELECT toDecimal32(1, 8) < 100

diff --git a/docs/en/data_types/enum.md b/docs/en/data_types/enum.md
@@ -2,7 +2,7 @@
 
 Enumerated type consisting of named values. 
 
-Named values must be delcared as `'string' = integer` pairs. ClickHouse stores only numbers, but supports operations with the values through their names.
+Named values must be declared as `'string' = integer` pairs. ClickHouse stores only numbers, but supports operations with the values through their names.
 
 ClickHouse supports:
 
@@ -106,7 +106,7 @@ The implicit default value is the value with the lowest number.
 
 During `ORDER BY`, `GROUP BY`, `IN`, `DISTINCT` and so on, Enums behave the same way as the corresponding numbers. For example, ORDER BY sorts them numerically. Equality and comparison operators work the same way on Enums as they do on the underlying numeric values.
 
-Enum values cannot be compared with numbers. Enums can be compared to a constant string. If the string compared to is not a valid value for the Enum, an exception will be thrown. The IN operator is supported with the Enum on the left hand side and a set of strings on the right hand side. The strings are the values of the corresponding Enum.
+Enum values cannot be compared with numbers. Enums can be compared to a constant string. If the string compared to is not a valid value for the Enum, an exception will be thrown. The IN operator is supported with the Enum on the left-hand side and a set of strings on the right-hand side. The strings are the values of the corresponding Enum.
 
 Most numeric and string operations are not defined for Enum values, e.g. adding a number to an Enum or concatenating a string to an Enum.
 However, the Enum has a natural `toString` function that returns its string value.

diff --git a/docs/en/data_types/fixedstring.md b/docs/en/data_types/fixedstring.md
@@ -14,7 +14,7 @@ The `FixedString` type is efficient when data has the length of precisely `N` by
 
 Examples of the values that can be efficiently stored in `FixedString`-typed columns:
 
-- Binary representation of IP addresses (`FixedString(16)` for IPv6).
+- The binary representation of IP addresses (`FixedString(16)` for IPv6).
 - Language codes (ru_RU, en_US ... ).
 - Currency codes (USD, RUB ... ).
 - Binary representation of hashes (`FixedString(16)` for MD5, `FixedString(32)` for SHA256).
@@ -48,7 +48,7 @@ WHERE a = 'b\0'
 └───┘
 ```
 
-This behavior differs from MySQL behavior for the `CHAR` type (where strings are padded with spaces, and the spaces are removed for output).
+This behaviour differs from MySQL for the `CHAR` type (where strings are padded with spaces, and the spaces are removed for output).
 
 Note that the length of the `FixedString(N)` value is constant. The [length](../query_language/functions/array_functions.md#array_functions-length) function returns `N` even if the `FixedString(N)` value is filled only with null bytes, but the [empty](../query_language/functions/string_functions.md#string_functions-empty) function returns `1` in this case.
 

diff --git a/docs/en/data_types/float.md b/docs/en/data_types/float.md
@@ -25,9 +25,9 @@ SELECT 1 - 0.9
 
 - The result of the calculation depends on the calculation method (the processor type and architecture of the computer system).
 - Floating-point calculations might result in numbers such as infinity (`Inf`) and "not-a-number" (`NaN`). This should be taken into account when processing the results of calculations.
-- When parsing floating point numbers from text, the result might not be the nearest machine-representable number.
+- When parsing floating-point numbers from text, the result might not be the nearest machine-representable number.
 
-## NaN and Inf {#data_type-float-nan-inf}
+## NaN and Inf { #data_type-float-nan-inf}
 
 In contrast to standard SQL, ClickHouse supports the following categories of floating-point numbers:
 

diff --git a/docs/en/data_types/index.md b/docs/en/data_types/index.md
@@ -1,8 +1,8 @@
-# Data Types {#data_types}
+# Data Types { #data_types}
 
-ClickHouse can store various types of data in table cells.
+ClickHouse can store various kinds of data in table cells.
 
-This section describes the supported data types and special considerations when using and/or implementing them, if any.
+This section describes the supported data types and special considerations for using and/or implementing them if any.
 
 
 [Original article](https://clickhouse.tech/docs/en/data_types/) <!--hide-->
diff --git a/docs/en/data_types/nested_data_structures/aggregatefunction.md b/docs/en/data_types/nested_data_structures/aggregatefunction.md
@@ -1,4 +1,4 @@
-# AggregateFunction(name, types_of_arguments...) {#data_type-aggregatefunction}
+# AggregateFunction(name, types_of_arguments...) { #data_type-aggregatefunction}
 
 The intermediate state of an aggregate function. To get it, use aggregate functions with the `-State` suffix. To get aggregated data in the future, you must use the same aggregate functions with the `-Merge`suffix.
 

diff --git a/docs/en/data_types/nullable.md b/docs/en/data_types/nullable.md
@@ -1,4 +1,4 @@
-# Nullable(TypeName) {#data_type-nullable}
+# Nullable(TypeName) { #data_type-nullable}
 
 Allows to store special marker ([NULL](../query_language/syntax.md)) that denotes "missing value" alongside normal values allowed by `TypeName`. For example, a `Nullable(Int8)` type column can store `Int8` type values, and the rows that don't have a value will store `NULL`.
 
@@ -10,7 +10,7 @@ A `Nullable` type field can't be included in table indexes.
 
 ## Storage features
 
-To store `Nullable` type values in table column, ClickHouse uses a separate file with `NULL` masks in addition to normal file with values. Entries in masks file allow ClickHouse to distinguish between `NULL` and default value of corresponding data type for each table row. Because of additional file, `Nullable` column consumes additional storage space compared to similar normal one.
+To store `Nullable` type values in a table column, ClickHouse uses a separate file with `NULL` masks in addition to normal file with values. Entries in masks file allow ClickHouse to distinguish between `NULL` and a default value of corresponding data type for each table row. Because of an additional file, `Nullable` column consumes additional storage space compared to a similar normal one.
 
 !!! info "Note"
     Using `Nullable` almost always negatively affects performance, keep this in mind when designing your databases.

diff --git a/docs/en/data_types/special_data_types/interval.md b/docs/en/data_types/special_data_types/interval.md
@@ -1,4 +1,4 @@
-# Interval {#data-type-interval}
+# Interval { #data-type-interval}
 
 The family of data types representing time and date intervals. The resulting types of the [INTERVAL](../../query_language/operators.md#operator-interval) operator.
 
@@ -32,7 +32,7 @@ SELECT toTypeName(INTERVAL 4 DAY)
 └──────────────────────────────┘
 ```
 
-## Usage Remarks {#data-type-interval-usage-remarks}
+## Usage Remarks { #data-type-interval-usage-remarks}
 
 You can use `Interval`-type values in arithmetical operations with [Date](../../data_types/date.md) and [DateTime](../../data_types/datetime.md)-type values. For example, you can add 4 days to the current time:
 

diff --git a/docs/en/data_types/uuid.md b/docs/en/data_types/uuid.md
@@ -1,4 +1,4 @@
-# UUID {#uuid-data-type}
+# UUID { #uuid-data-type}
 
 A universally unique identifier (UUID) is a 16-byte number used to identify records. For detailed information about the UUID, see [Wikipedia](https://en.wikipedia.org/wiki/Universally_unique_identifier).
 

diff --git a/docs/en/faq/general.md b/docs/en/faq/general.md
@@ -11,7 +11,7 @@ Distributed sorting is one of the main causes of reduced performance when runnin
 
 Most MapReduce implementations allow you to execute arbitrary code on a cluster. But a declarative query language is better suited to OLAP in order to run experiments quickly. For example, Hadoop has Hive and Pig. Also consider Cloudera Impala or Shark (outdated) for Spark, as well as Spark SQL, Presto, and Apache Drill. Performance when running such tasks is highly sub-optimal compared to specialized systems, but relatively high latency makes it unrealistic to use these systems as the backend for a web interface.
 
-## What If I Have a Problem with Encodings When Using Oracle Through ODBC? {#oracle-odbc-encodings}
+## What If I Have a Problem with Encodings When Using Oracle Through ODBC? { #oracle-odbc-encodings}
 
 If you use Oracle through the ODBC driver as a source of external dictionaries, you need to set the correct value for the `NLS_LANG` environment variable in `/etc/default/clickhouse`. For more information, see the [Oracle NLS_LANG FAQ](https://www.oracle.com/technetwork/products/globalization/nls-lang-099431.html).
 
@@ -21,7 +21,7 @@ If you use Oracle through the ODBC driver as a source of external dictionaries,
 NLS_LANG=RUSSIAN_RUSSIA.UTF8
 ```
 
-## How Do I Export Data from ClickHouse to a File? {#how-to-export-to-file}
+## How Do I Export Data from ClickHouse to a File? { #how-to-export-to-file}
 
 ### Using INTO OUTFILE Clause
 

diff --git a/docs/en/getting_started/install.md b/docs/en/getting_started/install.md
@@ -14,7 +14,7 @@ To run ClickHouse on processors that do not support SSE 4.2 or have AArch64 or P
 
 ## Available Installation Options
 
-### From DEB Packages {#install-from-deb-packages}
+### From DEB Packages { #install-from-deb-packages}
 
 It is recommended to use official pre-compiled `deb` packages for Debian or Ubuntu.
 
@@ -66,7 +66,7 @@ sudo yum install clickhouse-server clickhouse-client
 
 You can also download and install packages manually from here: <https://repo.clickhouse.tech/rpm/stable/x86_64>.
 
-### From tgz archives {#from-tgz-archives}
+### From tgz archives { #from-tgz-archives}
 
 It is recommended to use official pre-compiled `tgz` archives for all Linux distributions, where installation of `deb` or `rpm` packages is not possible.
 

diff --git a/docs/en/guides/apply_catboost_model.md b/docs/en/guides/apply_catboost_model.md
@@ -1,4 +1,4 @@
-# Applying a Catboost Model in ClickHouse {#applying-catboost-model-in-clickhouse}
+# Applying a Catboost Model in ClickHouse { #applying-catboost-model-in-clickhouse}
 
 [CatBoost](https://catboost.ai) is a free and open-source gradient boosting library developed at [Yandex](https://yandex.com/company/) for machine learning.
 
@@ -13,7 +13,7 @@ To apply a CatBoost model in ClickHouse:
 
 For more information about training CatBoost models, see [Training and applying models](https://catboost.ai/docs/features/training.html#training).
 
-## Prerequisites {#prerequisites}
+## Prerequisites { #prerequisites}
 
 If you don't have the [Docker](https://docs.docker.com/install/) yet, install it.
 
@@ -44,7 +44,7 @@ yandex/tutorial-catboost-clickhouse   latest              622e4d17945b        22
 $ docker run -it -p 8888:8888 yandex/tutorial-catboost-clickhouse
 ```
 
-## 1. Create a Table {#create-table}
+## 1. Create a Table { #create-table}
 
 To create a ClickHouse table for the train sample:
 
@@ -83,7 +83,7 @@ ENGINE = MergeTree ORDER BY date
 :) exit
 ```
 
-## 2. Insert the Data to the Table {#insert-data-to-table}
+## 2. Insert the Data to the Table { #insert-data-to-table}
 
 To insert the data:
 
@@ -112,7 +112,7 @@ FROM amazon_train
 +---------+
 ```
 
-## 3. Integrate CatBoost into ClickHouse {#integrate-catboost-into-clickhouse}
+## 3. Integrate CatBoost into ClickHouse { #integrate-catboost-into-clickhouse}
 
 !!! note "Note"
     **Optional step.** The Docker image contains everything you need to run CatBoost and ClickHouse.
@@ -154,7 +154,7 @@ The fastest way to evaluate a CatBoost model is compile `libcatboostmodel.<so|dl
 <models_config>/home/catboost/models/*_model.xml</models_config>
 ```
 
-## 4. Run the Model Inference from SQL {#run-model-inference}
+## 4. Run the Model Inference from SQL { #run-model-inference}
 
 For test model run the ClickHouse client `$ clickhouse client`.