RocksDB VS litestream

RocksDB: A high-performance embedded database optimized for multi-core CPUs and fast storage like SSDs. Its use of a log-structured merge-tree (LSM tree) makes it suitable for applications requiring high throughput and efficient storage, such as streaming data processing.

[RocksDB](https://rocksdb.org/) isn’t a distributed storage system, fwiw. It’s an embedded KV engine similar to LevelDB, LMDB, or really sqlite (though that’s full SQL, not just KV)

To output the top 3 rocks, our engine has to first store all the rocks in some sorted way. To do this, we of course picked RocksDB, an embedded lexicographically sorted key-value store, which acts as the sorting operation's persistent state. In our RocksDB state, the diffs are keyed by the value of weight, and since RocksDB is sorted, our stored diffs are automatically sorted by their weight.

Memgraph uses RocksDB as a key-value store for extending the capabilities of the in-memory database. Not to go into too many details about RocksDB, but let’s just briefly mention that it is based on a data structure called Log-Structured Merge-Tree (LSMT) (instead of B-Trees, typically the default option in databases), which are saved on disk and because of the design come with a much smaller write amplification than B-Trees.

The in-memory version of Memgraph uses Delta storage to support multi-version concurrency control (MVCC). However, for larger-than-memory storage, we decided to use the Optimistic Concurrency Control Protocol (OCC) since we assumed conflicts would rarely happen, and we could make use of RocksDB’s transactions without dealing with the custom layer of complexity like in the case of Delta storage.

Tuning RocksDB well is a very very hard challenge, and one that I am happy to not do day to day anymore. RocksDB is very powerful but it comes with other very sharp edges. Compaction is one of those, and all answers are likely workload dependent.

If you are worried about write amplification then leveled compactions are sub-optimal. I would try the universal compaction.

- https://github.com/facebook/rocksdb/wiki/Universal-Compactio...

It's fairly challenging to write a KV database, and takes several years of development to get the balance right between performance and reliability and avoiding data loss. Maybe read through the documentation for RocksDB https://github.com/facebook/rocksdb/wiki/RocksDB-Overview and watch the video on why it was developed and that may give you an impression of what is involved.

LMDB is much more sain in the sense that it supports real ACID transactions instead of savepoints for RocksDB. The latter is heavy and consumes a lot more memory for a lot less read throughput. However, RocksDB has a much better parallel and concurrent write story, where you can merge entries with merge functions and therefore write from multiple CPUs.

https://github.com/facebook/rocksdb/issues?q=is%3Aissue+clic...

Here are some bugs in JeMalloc:

Streamiz wrap a consumer, a producer, and execute the topology for each record consumed in the source topic. You can easily create stateless and stateful application. By default, each state store is a RocksDb state store persisted on disk.

I have not, but I keep meaning to collate everything I've learned into a set of useful defaults just to remind myself what settings I should be enabling and why.

Regarding Litestream, I learned pretty much all I know from their documentation: https://litestream.io/

This presentation is focused on the use-case of vertically scaling a single server and driving everything through that app server, which is running SQLite embedded within your application process.

This is the sweet-spot for SQLite applications, but there have been explorations and advances to running SQLite across a network of app servers. LiteFS (https://fly.io/docs/litefs/), the sibling to Litestream for backups (https://litestream.io), is aimed at precisely this use-case. Similarly, Turso (https://turso.tech) is a new-ish managed database company for running SQLite in a more traditional client-server distribution.

This post intends to help you setup replication for SQLite using Litestream.

wget https://github.com/benbjohnson/litestream/releases/download/v0.3.13/litestream-v0.3.13-linux-amd64.deb sudo dpkg -i litestream-v0.3.13-linux-amd64.deb

Litestream is a library that allows you to easily create backups. You can probably just do analytic queries on the backup data and reduce load on your server.

https://litestream.io/

One possible strategy is to have one directory/file per customer which is one SQLite file. But then as the user logs in, you have to look up first what database they should be connected to.

OR somehow derive it from the user ID/username. Keeping all the customer databases in a single directory/disk and then constantly "lite streaming" to S3.

Because each user is isolated, they'll be writing to their own database. But migrations would be a pain. They will have to be rolled out to each database separately.

One upside is, you can give users the ability to take their data with them, any time. It is just a single file.

[0]. https://litestream.io/

# Builder image FROM docker.io/alpine as BUILDER RUN apk add --no-cache curl jq tar RUN export LITESTREAM_VERSION=$(curl --silent https://api.github.com/repos/benbjohnson/litestream/releases/latest | jq -r .tag_name) && curl -L https://github.com/benbjohnson/litestream/releases/download/${LITESTREAM_VERSION}/litestream-${LITESTREAM_VERSION}-linux-amd64.tar.gz -o litestream.tar.gz && tar xzvf litestream.tar.gz # Main image FROM docker.io/louislam/uptime-kuma as KUMA ARG UPTIME_KUMA_PORT=3001 WORKDIR /app RUN mkdir -p /app/data COPY --from=BUILDER /litestream /usr/local/bin/litestream COPY litestream.yml /etc/litestream.yml COPY run.sh /usr/local/bin/run.sh EXPOSE ${UPTIME_KUMA_PORT} CMD [ "/usr/local/bin/run.sh" ]

Upstream Kuma uses a local SQLite database to store account data, configuration for services to monitor, notification settings, and more. To make sure that our data is available across redeploys, we will bundle Uptime Kuma with Litestream, a project that implements streaming replication for SQLite databases to a remote object storage provider. Effectively, this allows us to treat the local SQLite database as if it were securely stored in a remote database.

FROM docker.io/grafana/grafana-oss:9.5.12-ubuntu # Set USER to root escalating priviliges to perform installation of litestream and s6-overlay USER root RUN apt-get -qq update && \ apt-get -qq install -y xz-utils \ && rm -rf /var/libs/apt/lists/* # https://github.com/benbjohnson/litestream-s6-example/blob/main/Dockerfile # Download the static build of Litestream directly into the path & make it executable. ADD https://github.com/benbjohnson/litestream/releases/download/v0.3.11/litestream-v0.3.11-linux-amd64.tar.gz /tmp/litestream.tar.gz RUN tar -C / -xvzf /tmp/litestream.tar.gz ARG S6_OVERLAY_VERSION="3.1.5.0" # Download the s6-overlay for process supervision. ADD https://github.com/just-containers/s6-overlay/releases/download/v${S6_OVERLAY_VERSION}/s6-overlay-noarch.tar.xz /tmp RUN tar -C / -Jxpf /tmp/s6-overlay-noarch.tar.xz ADD https://github.com/just-containers/s6-overlay/releases/download/v${S6_OVERLAY_VERSION}/s6-overlay-x86_64.tar.xz /tmp RUN tar -C / -Jxpf /tmp/s6-overlay-x86_64.tar.xz # Copy s6 init & service definitions. COPY etc/s6-overlay /etc/s6-overlay # Copy Litestream configuration file. COPY etc/litestream.yml /etc/litestream.yml # The kill grace time is set to zero because our app handles shutdown through SIGTERM. ENV S6_KILL_GRACETIME=0 # Sync disks is enabled so that data is properly flushed. ENV S6_SYNC_DISKS=1 # Reset USER to 472 to reset the escalated privileges USER 472 # # Run the s6 init process on entry. ENTRYPOINT [ "/init" ]