https://github.com/clickhouse/ClickHouse There are stochastic regression methods in ClickHouse › stochasticLinearRegression › stochasticLogisticRegression Stochastic methods do support multiple factors. That’s That’s not the most important difference. 23 / 62 Stochastic linear regression in ClickHouse stochasticLinearRegression(parameters)(target, x1, ..., xN) Available parameters: › learning_rate › l2_regularization Nesterov All parameters are specified for stochastic gradient descent. Related wiki page: https://en.wikipedia.org/wiki/Stochastic_gradient_descent 24 / 62 Stochastic model with default parameters SELECT

https://github.com/clickhouse/ClickHouse There are stochastic regression methods in ClickHouse › stochasticLinearRegression › stochasticLogisticRegression Stochastic methods do support multiple factors. That’s That’s not the most important difference. 23 / 62 Stochastic linear regression in ClickHouse stochasticLinearRegression(parameters)(target, x1, ..., xN) Available parameters: › learning_rate › l2_regularization Momentum, Nesterov All parameters are specified for stochastic gradient descent. Related page: https://www.jianshu.com/p/9329294d56d2 24 / 62 Stochastic model with default parameters SELECT stochasticLinearRegression(

computing a tree traversal using a recursive query, you might want to order the results in either depth- first or breadth-first order. This can be done by computing an ordering column alongside the other nt. This approach merely provides a convenient way to order the results afterwards. To create a depth-first order, we compute for each result row an array of rows that we have visited so far. For example data FROM tree t, search_tree st WHERE t.id = st.link ) SELECT * FROM search_tree; To add depth-first ordering information, you can write this: WITH RECURSIVE search_tree(id, link, data, path)

computing a tree traversal using a recursive query, you might want to order the results in either depth-first or breadth-first order. This can be done by computing an ordering column alongside the other nt. This approach merely provides a convenient way to order the results afterwards. To create a depth-first order, we compute for each result row an array of rows that we have visited so far. For example data FROM tree t, search_tree st WHERE t.id = st.link ) SELECT * FROM search_tree; To add depth-first ordering information, you can write this: 135 Queries WITH RECURSIVE search_tree(id, link

WITH RECURSIVE search_graph(id, link, data, depth) AS ( SELECT g.id, g.link, g.data, 1 FROM graph g UNION ALL SELECT g.id, g.link, g.data, sg.depth + 1 FROM graph g, search_graph sg search_graph; This query will loop if the link relationships contain cycles. Because we require a “depth” output, just changing UNION ALL to UNION would not eliminate the looping. Instead we need to recognize search_graph(id, link, data, depth, path, cycle) AS ( SELECT g.id, g.link, g.data, 1, ARRAY[g.id], false FROM graph g UNION ALL SELECT g.id, g.link, g.data, sg.depth + 1, path ||

WITH RECURSIVE search_graph(id, link, data, depth) AS ( SELECT g.id, g.link, g.data, 1 FROM graph g UNION ALL SELECT g.id, g.link, g.data, sg.depth + 1 FROM graph g, search_graph sg search_graph; This query will loop if the link relationships contain cycles. Because we require a “depth” output, just changing UNION ALL to UNION would not eliminate the looping. Instead we need to recognize search_graph(id, link, data, depth, path, cycle) AS ( SELECT g.id, g.link, g.data, 1, ARRAY[g.id], false FROM graph g UNION ALL SELECT g.id, g.link, g.data, sg.depth + 1, path ||

computing a tree traversal using a recursive query, you might want to order the results in either depth-first or breadth-first order. This can be done by computing an ordering column alongside the other nt. This approach merely provides a convenient way to order the results afterwards. To create a depth-first order, we compute for each result row an array of rows that we have visited so far. For example data FROM tree t, search_tree st WHERE t.id = st.link ) SELECT * FROM search_tree; To add depth-first ordering information, you can write this: 137 Queries WITH RECURSIVE search_tree(id, link

computing a tree traversal using a recursive query, you might want to order the results in either depth- first or breadth-first order. This can be done by computing an ordering column alongside the other nt. This approach merely provides a convenient way to order the results afterwards. To create a depth-first order, we compute for each result row an array of rows that we have visited so far. For example data FROM tree t, search_tree st WHERE t.id = st.link ) SELECT * FROM search_tree; To add depth-first ordering information, you can write this: WITH RECURSIVE search_tree(id, link, data, path)

computing a tree traversal using a recursive query, you might want to order the results in either depth- first or breadth-first order. This can be done by computing an ordering column alongside the other nt. This approach merely provides a convenient way to order the results afterwards. To create a depth-first order, we compute for each result row an array of rows that we have visited so far. For example data FROM tree t, search_tree st WHERE t.id = st.link ) SELECT * FROM search_tree; To add depth-first ordering information, you can write this: WITH RECURSIVE search_tree(id, link, data, path)

computing a tree traversal using a recursive query, you might want to order the results in either depth-first or breadth-first order. This can be done by computing an ordering column alongside the other nt. This approach merely provides a convenient way to order the results afterwards. To create a depth-first order, we compute for each result row an array of rows that we have visited so far. For example data FROM tree t, search_tree st WHERE t.id = st.link ) SELECT * FROM search_tree; To add depth-first ordering information, you can write this: 138 Queries WITH RECURSIVE search_tree(id, link