You can use clickhouse-local to convert files between any of the input and output formats that ClickHouse supports (which is over 70 different formats!). In this article, we are convert a Parquet file in S3 into a CSV and JSON file.

This one is easy with clickhouse-local:

• Use the postgresql table function to read the data
• Use the INTO OUTFILE _filename_ FORMAT clause and specify the desired output format

The output format can be any of the supported ouput formats in ClickHouse. Let's look at a few examples...

The clickhouse-local tool makes it quick and easy to read data from MySQL and output the data into lots of different formats, including Parquet, CSV, and JSON. We are going to:

• Use the mysql table function to read the data
• Use the INTO OUTFILE _filename_ FORMAT clause and specify the desired output format

Problem remote() or remoteSecure() table function allows the access of remote table from another ClickHouse node.

When using these functions on a node that is located more than 100ms (latency wise) away from the remote node, it is common to encounter a timeout error.

You reached the parts_to_throw_insert setting on a MergeTree table.

ClickHouse supports a wide range of data formats for input and output. There are multiple JSON variations among them, but the most commonly used for data ingestion is JSONEachRow. It expects one JSON object per row, each object separated by a newline.

In no particular order, here are some handy queries for troubleshooting ClickHouse and figuring out what is happening.

Happy Pi Day! We thought it would be fun to calculate pi using SQL queries in ClickHouse. Here is what we came up with so far...

Question: When a source table has new rows inserted into it, those new rows are also sent to all of the materialized views of that source table. Are inserts into Materialized Views performed synchronously, meaning that once the insert is acknowledged successfully from the server to the client, it means that all Materialized Views have been fully updated and available for queries?

The new setting allow_asynchronous_read_from_io_pool_for_merge_tree allows the number of reading threads (streams) to be higher than the number of threads in the rest of the query execution pipeline.